Non-destructive Quality Assessment Research Articles

Rapid non-destructive monitoring and quality assessment of the fumigation process of Shanxi aged vinegar based on Vis-NIR hyperspectral imaging combined with multiple chemometric algorithms

The quality of the grains during the fumigation process can significantly affect the flavour and nutritional value of Shanxi aged vinegar (SAV). Hyperspectral imaging (HSI) was used to monitor the extent of fumigated grains, and it was combined with chemometrics to quantitatively predict three key physicochemical constituents: moisture content (MC), total acid (TA) and amino acid nitrogen (AAN). The noise reduction effects of five spectral preprocessing methods were compared, followed by the screening of optimal wavelengths using competitive adaptive reweighted sampling. Support vector machine classification was employed to establish a model for discriminating fumigated grains, and the best recognition accuracy reached 100%. Furthermore, the results of partial least squares regression slightly outperformed support vector machine regression, with correlation coefficient for prediction (Rp) of 0.9697, 0.9716, and 0.9098 for MC, TA, and AAN, respectively. The study demonstrates that HSI can be employed for rapid non-destructive monitoring and quality assessment of the fumigation process in SAV.

Electrical Properties for Non-destructive Determination of Free Fatty Acid and Moisture Content in Oil Palm Fruit

FFA content is one of the essential qualities of oil palm fruit. FFA content exceeding 5% is considered unsuitable for human consumption. Commonly, FFA content is determined by chemical methods in the laboratory, but it is destructive, time-consuming, and costly. Several non-destructive methods have been investigated, but a maturity prediction approach has been primarily used with no direct relation to FFA content. Thus, there is a pressing need for a non-destructive framework to assess the FFA levels in the oil palm fruit directly. An attempt has been explored using a non-destructive palm fruit quality assessment that relied on electrical properties (impedance, admittance, resistance, and capacitance) in the frequency range from 50 Hz to 5 MHz was investigated to predict FFA and moisture content directly. Two statistical analyses were employed: stepwise multiple linear regressions (MLR) and artificial neural networks (ANN) to calibrate and validate electrical properties with FFA and moisture content. The best-performing models ANN showcased significant results: r= 0.96, R2=0.92, SEC at 0.86%, SEP at 0.97%, CV at 19.45%, consistency at 88.54, and RPD at 3.43 for FFA prediction, and r= 0.99, R2=0.98, SEC at 3.09 %, SEP at 3.46 %, CV at 5.44 %, consistency at 89.08 and RPD at 7.02 for predicting moisture content. In modeling oil palm quality determination, applying the ANN method significantly improved model performances, demonstrating its efficacy in predicting non-destructively both FFA levels based on admittance and moisture content based on impedance.

Dual-aspect attention spatial-spectral transformer and hyperspectral imaging: A novel approach to detecting Aspergillus flavus contamination in peanut kernels

In this study, an innovative dual-aspect attention spatial-spectral transformer (DAASST) was introduced to advance postharvest quality control by the detection of Aspergillus flavus contamination and the accurate identification of contamination times in peanut kernels. The critical importance of maintaining postharvest quality and safety in nuts was recognized, with hyperspectral imaging technology being leveraged due to its great potential in non-destructive testing and quality assessment of nuts. At the heart of DAASST's innovation, an enhanced transformer architecture that incorporated an attention fusion mechanism was employed for the effective integration of the extracted features. This sophisticated integration not only improved the model’s performance but also was significantly surpassed by the capabilities of traditional machine learning methods in the context of postharvest biology and technology. Exceptional accuracy was demonstrated in testing, with 99.40% achieved in detecting Aspergillus flavus contamination and a remarkable 100% in distinguishing between different contamination times. Significant contributions to the field of postharvest biology and technology were made by merging cutting-edge feature extraction techniques, attention mechanisms, and transformer architecture to refine hyperspectral image analysis for postharvest quality control. The proven effectiveness of the DAASST in accurately detecting Aspergillus flavus and determining contamination times in peanut kernels highlighted its potential as a valuable tool for ensuring the safety and quality of postharvest nuts.

Nondestructive detection of fruit fly (Bactrocera dorsalis) infestation in Alphonso and Totapuri varieties of mango fruits by thermal imaging technique

AbstractThermal imaging is a noncontact and nondestructive sensing technique that provides thermal signatures of objects by detecting the infrared radiations emitted by them above 1 K. The nondestructive detection of internal defects and infestations has vast applications in the Food Industry. In the present experiment, a C3 thermal camera (FLIR systems) having a thermal sensitivity of <0.10°C was used for the detection of fruit fly infestation in mango fruits. The healthy mango fruits of both varieties had a uniform surface temperature with a variation of only ±0.1°C. The maximum temperature difference between the healthy and fruit fly infested regions in fruit fly infested Totapuri mango was found to be 4.56°C in the heated and dark conditions. The maximum difference in the surface temperature of 4.3°C was observed between the healthy and infested regions of the infested Alphonso mango under heated and dark conditions. The difference in temperature between the healthy and infested regions of mangoes can be attributed to the changes in the internal structure, thermal properties, and biochemical properties of the healthy and infested regions of the infested mango fruits. The statistical analysis showed a significant difference (p < 0.05) in surface temperature between the infested and healthy regions of fruit fly infested Alphonso and Totapuri mango fruits. This envisages that nondestructive fruit fly infestation in Alphonso and Totapuri varieties of mango fruits can be detected under active thermography.Practical ApplicationsThe quality of mango fruit can be described in terms of external and internal quality. The external quality parameters such as color, size, and weight are usually considered during the sorting of mangoes. The internal defects such as diseases, physiological disorders, and damage due to pests are also significant in mango processing. The visual inspection methods for detecting pest infestation in fruits are nonreliable, time‐consuming, and subjective. Therefore, there is a need for efficient, reliable, and accurate methods to detect fruit fly infestation in mango fruits. A nondestructive method for the detection of fruit fly infestation has the potential to increase fruit quality and acceptability, thereby benefiting processors, traders, and consumers. Thermal imaging has a distinct advantage over other nondestructive quality assessment techniques as it is noncontact, practically feasible, and economically viable.

Recognition performance of different artificial neural networks for distinguishing banana slices subjected to different combinations of pretreatment and microwave drying

This study was aimed at assessing the effect of microwave drying at 100, 200, or 300 W on the quality of Cavendish banana slices without pretreatment and with pretreatment using 5% ascorbic acid solution, 5% citric acid solution, 5% gum arabic solution, and ultrasound. Banana slices were imaged using a digital single-lens reflex (SLR) camera. The acquired images were processed to extract texture parameters. The classification models were developed based on image texture parameters selected from a big dataset of 2172 textures of images in different color channels using artificial neural networks. Wide Neural Network, Bilayered Neural Network, Medium Neural Network and three classifiers from the group of function, such as RBF (Radial basis function) Network, Multilayer Perceptron, and WiSARD were applied. Banana slices belonging to 15 classes with different combinations of pretreatment and microwave drying were distinguished with an average accuracy of up to 97.2% for a model built using Multilayer Perceptron. For most models, banana samples microwave-dried at 200 W without pretreatment were classified with the highest correctness. The performed study revealed that the objective, non-destructive, correct, and robust quality assessment of pretreated and microwave-dried banana slices may be performed using image processing and artificial intelligence.

Nondestructive quality assessment and maturity classification of loquats based on hyperspectral imaging

The traditional method for assessing the quality and maturity of loquats has disadvantages such as destructive sampling and being time-consuming. In this study, hyperspectral imaging technology was used to nondestructively predict and visualise the colour, firmness, and soluble solids content (SSC) of loquats and discriminate maturity. On comparison of the performance of different feature variables selection methods and the calibration models, the results indicated that the multiple linear regression (MLR) models combined with the competitive adaptive reweighting algorithm (CARS) yielded the best prediction performance for loquat quality. Particularly, CARS-MLR models with optimal prediction performance were obtained for the colour (R2P = 0.96, RMSEP = 0.45, RPD = 5.38), firmness (R2P = 0.87, RMSEP = 0.23, RPD = 2.81), and SSC (R2P = 0.84, RMSEP = 0.51, RPD = 2.54). Subsequently, distribution maps of the colour, firmness, and SSC of loquats were obtained based on the optimal CARS-MLR models combined with pseudo-colour technology. Finally, on comparison of different classification models for loquat maturity, the partial least square discrimination analysis model demonstrated the best performance, with classification accuracies of 98.19% and 97.99% for calibration and prediction sets, respectively. This study demonstrated that the hyperspectral imaging technique is promising for loquat quality assessment and maturity classification.

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.

A Review of the Use of Near-Infrared Hyperspectral Imaging (NIR-HSI) Techniques for the Non-Destructive Quality Assessment of Root and Tuber Crops

Hyperspectral imaging (HSI) is one of the most often used techniques for rapid quality evaluation for various applications. It is a non-destructive technique that effectively evaluates the quality attributes of root and tuber crops, including yam and cassava, and their food products. Hyperspectral imaging technology, which combines spectroscopy and imaging principles, has an advantage over conventional spectroscopy due to its ability to simultaneously evaluate the physical characteristics and chemical components of various food products and specify their spatial distributions. HSI has demonstrated significant potential for obtaining quick information regarding the chemical composition of the root and tuber, such as starch, protein, dry matter, amylose, and soluble sugars, as well as physical characteristics such as textural properties and water binding capacity. This review highlights the principles of near-infrared hyperspectral imaging (NIR-HSI) techniques combined with relevant image processing tools. It then provides cases of its application in determining crucial biochemical quality traits and textural attributes of roots and tuber crops, focusing on cassava and yam. The need for more information on using NIR-HSI in the quality evaluation of yam and cassava was underscored. It also presents the challenges and prospects of this technology.

The Need for Machines for the Nondestructive Quality Assessment of Potatoes with the Use of Artificial Intelligence Methods and Imaging Techniques.

This article describes chemical and physical parameters, including their role in the storage, trade, and processing of potatoes, as well as their nutritional properties and health benefits resulting from their consumption. An analysis of the share of losses occurring during the production process is presented. The methods and applications used in recent years to estimate the physical and chemical parameters of potatoes during their storage and processing, which determine the quality of potatoes, are presented. The potential of the technologies used to classify the quality of potatoes, mechanical and ultrasonic, and image processing and analysis using vision systems, as well as their use in applications with artificial intelligence, are discussed.

Towards sweetness classification of orange cultivars using short-wave NIR spectroscopy

The global orange industry constantly faces new technical challenges to meet consumer demands for quality fruits. Instead of traditional subjective fruit quality assessment methods, the interest in the horticulture industry has increased in objective, quantitative, and non-destructive assessment methods. Oranges have a thick peel which makes their non-destructive quality assessment challenging. This paper evaluates the potential of short-wave NIR spectroscopy and direct sweetness classification approach for Pakistani cultivars of orange, i.e., Red-Blood, Mosambi, and Succari. The correlation between quality indices, i.e., Brix, titratable acidity (TA), Brix: TA and BrimA (Brix minus acids), sensory assessment of the fruit, and short-wave NIR spectra, is analysed. Mix cultivar oranges are classified as sweet, mixed, and acidic based on short-wave NIR spectra. Short-wave NIR spectral data were obtained using the industry standard F-750 fruit quality meter (310–1100 nm). Reference Brix and TA measurements were taken using standard destructive testing methods. Reference taste labels i.e., sweet, mix, and acidic, were acquired through sensory evaluation of samples. For indirect fruit classification, partial least squares regression models were developed for Brix, TA, Brix: TA, and BrimA estimation with a correlation coefficient of 0.57, 0.73, 0.66, and 0.55, respectively, on independent test data. The ensemble classifier achieved 81.03% accuracy for three classes (sweet, mixed, and acidic) classification on independent test data for direct fruit classification. A good correlation between NIR spectra and sensory assessment is observed as compared to quality indices. A direct classification approach is more suitable for a machine-learning-based orange sweetness classification using NIR spectroscopy than the estimation of quality indices.

Non-destructive quality assessment method for Korla fragrant pears based on electrical properties and adaptive neural-fuzzy inference system

This study aimed to achieve the rapid and non-destructive quality assessment of Korla fragrant pears, thereby maximising the value of damaged pears and improving the market supply–demand balance. The electrical characteristics of damaged Korla fragrant pears were measured using a fruit electrical property detector and the trends in the quality of the fragrant pears within their shelf life were analysed. The quality of damaged Korla fragrant pears was predicted by using an adaptive neural-fuzzy inference system (ANFIS). Electrical properties were used as inputs to the system and quality was the output of the system. A total of eight functional models with different degrees of membership were constructed and the optimal one was selected. The results demonstrated that the equivalent parallel capacitance (Cp) of the damaged Korla fragrant pears increased gradually throughout their shelf life, while equivalent parallel resistance (Rp) and complex impedance (Z) decreased. Additionally, the green–red difference (a*) increased gradually, while hardness and soluble solid content (SSC) decreased continuously. Based on the combination of electrical properties and the ANFIS, gbellmf was the optimal model to determine the SSC of the damaged Korla fragrant pears (R2 = 0.9123) and gaussmf was the optimal model to determine the hardness and a* of the damaged Korla fragrant pears (R2 = 0.9112 and 0.8853, respectively). It was concluded that the combined use of electrical properties and the ANFIS method enabled the successful quality assessment of fragrant pears. This approach was conducive to facilitating the optimisation and updating of non-destructive quality assessment technologies for fragrant pears, promoting quality and efficiency improvement in the fragrant pear industry.

Current advances in detection of abnormal egg: a review.

Internal and external defects of eggs should be detected to prevent cross-contamination of intact eggs by abnormal eggs during storage. Emerging detection technologies for abnormal eggs were introduced as an alternative to human inspection. The advanced technologies could rapidly detect abnormal eggs. Abnormal egg detection technologies using acoustic response, machine vision, and spectroscopy have been commercialized in the poultry industry. Non-destructive egg quality assessment methods meanwhile could preserve the value of eggs and improve detection efficiency. In order to improve detection efficiency, it is essential to select a proper algorithm for classifying the types of abnormal eggs. This review deals with the performance of the detection technologies for various types of abnormal eggs in recently published resources. In addition, the discriminant methods and detection algorithms of abnormal eggs reported in the published literature were investigated. Although the majority of the studies were conducted on a laboratory scale, the developed detection technologies for internal and external defects in eggs were technically feasible to obtain the excellent detection accuracy. To apply the developed detection technologies to the poultry industry, it is necessary to achieve the detection rates required from the industry.

Nondestructive detection and grading of flesh translucency in pineapples with visible and near-infrared spectroscopy

Rapid, accurate, and nondestructive internal quality detection for large and rough surface fruit, such as translucency in pineapples, is challenging. In this paper, a visible and near infrared (VIS/NIR) spectrum-based platform is proposed for optimized detection of pineapple translucency. The internal quality of three batches of samples harvested at the same maturity but on different dates (early, middle, and mid to late harvest stage) were acquired with different spectral settings: VIS to shortwave NIR(400–1100 nm), NIR (900–1700 nm) and VIS/NIR (400–1700 nm). The pineapple samples were manually cut open and divided into three translucency degrees (no, slight, and heavy), according to marketing standards. The Savitzky Golay (SG) and standard normal variate (SNV) were applied to remove jitter and scattering noise, respectively. The successive projections algorithm, principal component analysis and Euclidean distance were combined for feature extraction and measurement, followed by data modeling using the partial least squares regression and probabilistic neural network (PNN). Data correction, data supplementation, and a combination of these were applied for model updating. Experimental results showed that the optimal solution for pineapple translucency detection was to use 400–1100 nm spectrum with SG, SNV, PNN and data supplementation for model updating. With only the first and second batch of samples used for modeling (validation set accuracy 91.2 %) and updating (validation set accuracy 100 %), the detection accuracy on the third batch samples was 100 %. The proposed methodologies therefore can be used as rapid, nondestructive, and cost-effective tools to detect pineapple translucency to guarantee the marketing of high-quality fruit, which can also guide the postharvest treatment for the pineapple industry to improve market competitiveness as well as to benefit nondestructive quality assessment of other large fruit.

Physics-informed ensemble learning for online joint strength prediction in ultrasonic metal welding

Ultrasonic metal welding (UMW) is a promising solid-state joining technology that enables innovative and sustainable manufacturing. Despite possessing numerous advantages, UMW has a narrow operating window and is susceptible to both internal and external disturbances. As such, industrial scale UMW production calls for efficient, effective, and non-destructive joint quality assessment. To this end, this paper develops a novel hierarchical physics-informed ensemble learning (PIEL) framework that uses both physical knowledge and online sensing data for accurate online prediction of UMW joint strength. The PIEL framework decomposes the joint strength variability into a physics-informed global trend and a data-driven residual. The global trend is attributed to controllable or measurable welding conditions and is typically perceived as a large-scale variability. The data-driven residual is observed as a small-scale component when identical welding conditions are applied and can be captured by online sensing data. Drawing on this decomposition, hierarchical prediction models can be established to simultaneously account for both types of variabilities. As an essential component of the PIEL methodology, a highly efficient feature extraction procedure is developed using discrete wavelet transformation (DWT). The DWT-based feature extraction procedure is able to automatically extract key low-dimensional information from high-dimensional sensing signals, thus offering improved efficiency and effectiveness compared to conventional feature engineering approaches. Two real-world case studies with distinct physical setups are presented to demonstrate the effectiveness of the PIEL framework. The first case study investigates the influence of tool degradation and uses a dataset consisting of 200 welding cycles generated under four tool conditions. In the second case study, welding parameters including time, amplitude, and pressure are varied to generate 240 welds. When compared against multiple state-of-the-art baseline methods, PIEL consistently achieves superior prediction accuracy. Further, it is shown that by integrating physical knowledge, PIEL can effectively avoid overfitting and achieve excellent modeling robustness and data efficiency. While developed in the context of UMW, the PIEL framework is readily extensible to various other manufacturing processes.

Nondestructive visualization of graphene on Pt with methylene blue surface modification

Efficient large-scale nondestructive quality assessment of graphene on Pt is essential to the in-depth growth research and practical applications of graphene. Here, we present a very simple method for directly observing the domains and defects in graphene on Pt using an ordinary optical microscope. This was achieved by modifying graphene on Pt using methylene blue (MB). Because the chemical activities of graphene and Pt surface differ significantly, the adsorption and reaction of MB on graphene and platinum surface differ. We can determine the distribution of graphene crystal domains and defects by comparing the colors in the optical images. In addition, this characterization method causes no obvious damage to the Pt substrate and graphene. Moreover, it does not affect the recycling of the substrate or the subsequent characterization or application of graphene. Our study provides a nondestructive method for measuring the quality of graphene on Pt on a large scale, as well as a reference for the characterization and doping of other two-dimensional materials.

Investigating dielectric spectroscopy and soft sensing for nondestructive quality assessment of engineered tissues

Non-destructive, inline quality monitoring techniques that can overcome the limitations of traditional, offline assays are essential to support the scale-up production of tissue engineered medical products (TEMP). In this work, we investigate a new soft-sensing approach with non-destructive dielectric spectroscopy (DS) that synergistically utilizes inline sensor data and predictive analytics to estimate unmeasured TEMP quality profiles. First, the performance of DS during the assessment of gelatin methacrylate (GelMA) constructs containing human adipose-derived stem cells was investigated in comparison to a traditional biochemical assay. The effects of two critical biofabrication parameters (photocrosslinking duration and volume of growth media) on a key scalar metric (Δϵ) were determined over 11 days of in vitro culture, where the metric was associated with the permittivity response of cells to alternating electric fields during DS and corresponding cellular metabolic activity. To enable accurate quality prediction while minimizing direct data collection to reduce the risk of cytotoxicity from prolonged exposure to the DS sensor electrodes and electric fields, we then developed a bilinear basis mixed model (BBMM) as a soft sensor. With comprehensive consideration of different variation sources, this model was designed to estimate missing permittivity profiles of constructs based on the measured DS dataset and biofabrication parameters. Results of benchmarking showed that BBMM outperformed state-of-the-art vector-prediction methods from literature in two different missing data estimation mechanisms. The high-accuracy BBMM provides a novel DS-driven soft sensing system as an inline monitoring tool suitable for scaled-up or scaled-out TEMP production systems.

Low-frequency ultrasound as a tool for quality control of soft-bodied raw ewe's milk cheeses

The relationship between the quality attributes and the ultrasound parameters of Protected Designation of Origin (PDO) soft-bodied cheeses from “Torta del Casar” and “Queso de la Serena” was studied. Cheese samples were taken from six different dairy industries (three for each PDO) in two different seasons. Ultrasound parameters related to velocity, attenuation and frequency were calculated and correlated with the physico-chemical, microbiological, rheological and sensorial properties of the cheeses. Several ultrasonic parameters showed a high correlation with changes in the physico-chemical, textural and sensorial properties of this product, which were partially associated with microbial activity. Among these, stand out the negative correlation of the sensorial descriptor “firmness” with bulk modulus ( κ ). The predictive models achieved by the multivariate linear regression method (MLR) showed the highest correlation coefficients for firmness by texture compression analysis (TCA) and the sensory descriptor “intensity”, including as the explanatory variables for both models, fast Fourier transform (FFT) frequencies and attenuation parameters. This work highlights the on-line capability of ultrasound for non-destructive quality assessment of this traditional and heterogeneous soft cheese based on the predictive potential of several secondary ultrasonic parameters. • Soft-bodied raw ewe's milk cheeses granted the PDO label were evaluated. • Relationships among cheese quality attributes and ultrasound parameters were studied. • Ultrasonic parameters showed correlations with physico-chemical, textural and sensorial properties. • A predictive model was developed by the multivariate linear regression method. • Ultrasound parameters are useful tools for quality control of soft-bodied cheeses.

Variable selection for the determination of the soluble solid content of potatoes with surface impurities in the visible/near-infrared range

Non-destructive quality assessment of the inner content of potatoes is a key challenge in automatic grading of agricultural quality, especially when potatoes have surface impurities. This study compares different partial least-square regression (PLSR) models for the prediction of soluble solid content (SSC) of potatoes under conditions of surface cleanliness and surface impurities. Smoothing and spectral preprocessing with first-order derivatives and variable sorting for normalization (VSN) can effectively eliminate spectral noise. Variable selection algorithms were used to extract effective variables in order to further optimise the prediction models. The results showed that the method of the variable combination population analysis—iteratively retains informative variables (VCPA-IRIV) is the best method for selecting valid variables, and that the 35-variable VCPA-IRIV-PLSR prediction model could predict the potato SSC with a predictive correlation coefficient ( R p ), root-mean-square error of prediction ( RMSE P ), and residual predictive deviation ( RPD ) values of 0.831, 0.461 ○ Brix and 1.798, respectively. Therefore, the experimental results show the feasibility and applicability of the proposed SSC prediction method for potatoes with surface impurities using near-infrared spectroscopy. • Non-destructive detection of uncleaned potatoes (with surface impurities). • Good PLSR prediction model of potato SSC based on surface impurities. • Processing spectral data with multiple preprocessing algorithms to eliminate noise. • Proposed optimised variable selection algorithms for effective wavelength selection. • Application of VCPA-IRIV algorithm.

Food-scanner applications in the fruit and vegetable sector

In the past few years, portable and smartphone-based diagnostic technologies have found their way into the agri-food industry. The aim of this research was to evaluate the performance of portable near-infrared (NIR) spectrometers, so called food-scanners, with regard to their predictive accuracy of important quality parameters of fruit and vegetables. Food-scanner measurements were performed in combination with destructive measurements of the corresponding quality trait (sugar content, dry matter, relative water content) on a wide range of produce from the fruit and vegetable assortment. This study evaluated dry matter content of apple, avocado, blueberry, table grape and tangerine, which yielded cross validation results (r²) of up to 0.95, 0.87, 0.94, 0.92 and 0.92 respectively. Furthermore, the evaluation of food-scanner spectra for the prediction of sugar content of blueberry, kiwi, mango, persimmon, table grape, tangerine and tomato yielded cross validations (r²) of up to 0.95, 0.84, 0.80, 0.75, 0.95, 0.93, and 0.87. Furthermore, relative water content of ginger obtained a cross validation correlation of r² = 0.91. The results show that these traits can be predicted with a high degree of accuracy using non-destructive measurements performed with three commercially available food-scanners SCiO™, F-750 Produce Quality Meter, and H-100F. Consequently, food-scanners can be used as objective measurement tools along the supply chain of fresh produce to quickly determine fruit quality. In addition, a practical example shows the potential of these instruments for non-destructive quality assessment in incoming goods control at fruit and vegetable wholesalers over a time period of several weeks. Furthermore, possible areas of application of food-scanners along the supply chain of fresh produce are discussed, possibilities for practical applications are presented and time-saving means are highlighted.

Non-destructive quality assessment of bio-engineering parts using Industrial Micro X-ray Computed Tomography: A review

Bio-engineering parts are widely used in hearing aid applications, dental applications, inhalation therapy blends, etc. Conventional tactile measurement techniques offer a limitation in the quality assessment of such parts due to the complex shape of the part and also due to their inability to measure internal features in a three-dimensional space. Industrial micro X-ray computed tomography (μXCT) is a robust characterization technique designed to maintain part quality, facilitate precision manufacturing, and reduce material waste due to part rejection. This paper highlights the limitations and challenges of conventional measurement techniques in the assessment of the quality of bio-engineering components. Capabilities of μXCT are presented and its application in the bio-engineering sector is discussed in this work.