Non-destructive Technique Research Articles

Investigation of mechanical characteristics and sustainable practices in concrete incorporating recycled marble waste

Due to the rapid expansion of infrastructure, urbanization, and industry, there is a constant need for concrete worldwide. However, the expansion of the concrete sector increases pressure on natural resources, endangering the balance of the ecosystem. Thus, it would be advantageous to meet current concrete needs without reducing production quality by incorporating recycled materials into concrete mixes. In this work, we investigate the mechanical characteristics of environmentally friendly concrete using recycled marble powder (WMP) partially replacing cement and marble coarse aggregates (MCA) as an alternative to natural coarse aggregates. The qualities of the concrete were evaluated using a combination of destructive and non-destructive testing techniques. In this study, three series of mixes were prepared. The first series (S1) is composed of 100% natural coarse aggregates. In the second series (S2), 50% of the natural coarse aggregates are substituted for marble coarse aggregates, while the third series (S3) is composed of 100% marble coarse aggregates. In each of the three series, Marble powder was used in place of cement at a percentage ranging from 5% to 20%, increasing by 2.5%. Concrete mixes were developed and evaluated with different levels of marble waste substitution to compare their compressive strengths and workability to those of traditional concrete made from 100% natural aggregates. Simultaneously, the strength characteristics were measured using the Schmidt hammer and ultrasonic velocity tests. As was mentioned, in the three series, when Cement is replaced with marble powder, the slump decreases, even though the incorporation of marble coarse aggregates tends to increase this workability, while the compressive strength shows an increase of 28.66% and 31.63% compared to the control mix for 50% and 100% substitute of natural coarse aggregates with marble coarse aggregates, respectively. It is also noteworthy that concrete containing marble waste exhibits normal ultrasonic velocity. For concrete mixtures of series (S1), (S2), and (S3), respectively, the velocity of ultrasonic pulses increased by 18.20%, 27.44%, and 27.98%, while the Schmidt Rebound Number increased by 10.13%, 14.52%, and 21.37%, respectively, in contrast to the values of the control mixture. Additionally, the reliability of results from the universal testing machine (UTM) was validated through correlation analysis of compressive strength measurements obtained by various methods. The study's findings are intriguing and underscore the potential of employing marble waste as an alternative to natural aggregates.

Unravelling the electrochemical impedance spectroscopy of hydrogenated amorphous silicon cells for photovoltaics

Abstract This research reveals the application of electrochemical impedance spectroscopy (EIS) in analyzing and improving the performance of hydrogenated amorphous silicon (a-Si: H) based photovoltaic cells. As a non-destructive technique, EIS provides deep insight into the electrochemical characteristics of photovoltaic cells, including series resistance, layer capacitance, recombination mechanisms, and charge transport. The impedance data is obtained and analyzed using small AC potential signals at various frequencies via Nyquist diagrams and Bode plots. This analysis allows the identification of resistive and capacitive elements as well as the evaluation of the quality of the interface between the active layer and the electrode. The results show that EIS can identify internal barriers that reduce the efficiency of a-Si: H solar cells, such as dominant recombination mechanisms and inefficient charge transport. Using equivalent circuit models, electrochemical parameters are extracted to reveal cell behavior and performance. In addition, these results also confirm that EIS is an important tool in design optimization and performance improvement of a-Si: H photovoltaic cells, providing a solid scientific basis for the development of more efficient and sustainable solar cell technology. These findings contribute to efforts to increase solar energy efficiency, supporting broader and more effective use of photovoltaic technology in meeting global sustainable energy needs.

Fracture mode classification of UHPC based on deep learning and wavelet time–frequency spectrum derived from acoustic emission waveform

Fracture mode categorization is essential in identifying the failure stage, evaluating the damage characteristics, and providing references for structural maintenance. Acoustic emission (AE), as a passive nondestructive technique, presents great potential in damage characterization of cementitious materials and structures. In this study, a convolutional neural network (CNN) and gated recurrent unit (GRU) integrated model for tensile, shear, and mixed cracking mode classification of ultra-high-performance concrete (UHPC) was proposed and verified based on AE waveform features. First, four-point bending and direct shear tests for UHPC specimens were carried out, respectively, to generate AE waveform signals corresponding to different cracking modes (tensile, shear, and mixed ones). Subsequently, the collected one-dimensional AE signals were converted into two-dimensional time–frequency spectrum by continuous wavelet transform. And an AE dataset was constructed after manual labeling based on the corresponding relationships between cracking modes and wavelet time–frequency spectra. Then, an automatic CNN-GRU automatic classification model was established using wavelet time–frequency spectrum images as input variables, where the CNN extracts spatial image features, and the GRU screens time dependencies and realizes final classification. The proposed CNN-GRU model achieves an accuracy of 96.17% for cracking mode classification of UHPC, which is superior both in computational accuracy and efficiency to lightweight CNN and AlexNet. Classification outcomes for UHPC specimen under four-point bending and direct shear tests revealed the proportion of tensile cracks progressively decreased with the increase of damage, while shear cracks exhibited the opposite trend. The percentage of mixed mode cracks maintained relatively stable throughout the failure stages. The model proposed in this study presents favorable performance to reveal the underlying damage evolution mechanism during UHPC fracture, which can also provide references for the cracking mode classification of other cementitious materials and structures.

Nondestructive Identification of Internal Potato Defects Using Visible and Short-Wavelength Near-Infrared Spectral Analysis

Potatoes are a staple food crop consumed worldwide, with their significance extending from household kitchens to large-scale food processing industries. Their market value and quality are often compromised by various internal defects such as pythium, bruising, internal browning, hollow heart, gangrene, blackheart, internal sprouting, and dry rot. This study aimed to classify internal-based defects and investigate the quantification of internal defective areas in potatoes using visible and short-wavelength near-infrared spectroscopy. The acquisition of the spectral data of potato tubers was performed using a spectrometer with a wavelength range of 400–1100 nm. The classification of internal-based defects was performed using partial least squares discriminant analysis (PLS-DA), while the quantification of the internal defective area was based on partial least squares regression (PLSR). The PLS-DA double cross-validation accuracy for the distinction between non-defective and all internally defective potatoes was 90.78%. The double cross-validation classification accuracy achieved for pythium, bruising, and non-defective categories was 91.03%. The internal defective area model based on PLSR achieved a correlation coefficient of double cross-validation of 0.91 and a root mean square error of double cross-validation of 0.85 cm2. This study makes a valuable contribution to advancing nondestructive techniques for evaluating internal defects in potatoes.

Non-Destructive Wood Analysis Dataset: Comparing X-Ray and Terahertz Imaging Techniques

Wood density measurement plays a crucial role in assessing wood quality and predicting its mechanical performance. This dataset was collected to compare the accuracy and reliability of two non-destructive techniques, X-rays and terahertz waves, for measuring wood density. While X-rays have been commonly used in the industry due to their effectiveness, they pose health risks due to ionizing radiation. Terahertz waves, on the other hand, are non-ionizing and offer high spatial resolution. This article presents a database of wood samples measurements obtained using both techniques, on the same 110 samples with a fine location of the measuring points, on a wide range of wood species (tropical and temperate ones) and densities, from 111 kg·m−3 to 1086 kg·m−3. The database includes X-ray and terahertz scans, sample dimensions, moisture content, and color photographs.

Farm to fork applications: how vibrational spectroscopy can be used along the whole value chain?

Vibrational spectroscopy is a nondestructive analysis technique that depends on the periodic variations in dipole moments and polarizabilities resulting from the molecular vibrations of molecules/atoms. These methods have important advantages over conventional analytical techniques, including (a) their simplicity in terms of implementation and operation, (b) their adaptability to on-line and on-farm applications, (c) making measurement in a few minutes, and (d) the absence of dangerous solvents throughout sample preparation or measurement. Food safety is a concept that requires the assurance that food is free from any physical, chemical, or biological hazards at all stages, from farm to fork. Continuous monitoring should be provided in order to guarantee the safety of the food. Regarding their advantages, vibrational spectroscopic methods, such as Fourier-transform infrared (FTIR), near-infrared (NIR), and Raman spectroscopy, are considered reliable and rapid techniques to track food safety- and food authenticity-related issues throughout the food chain. Furthermore, coupling spectral data with chemometric approaches also enables the discrimination of samples with different kinds of food safety-related hazards. This review deals with the recent application of vibrational spectroscopic techniques to monitor various hazards related to various foods, including crops, fruits, vegetables, milk, dairy products, meat, seafood, and poultry, throughout harvesting, transportation, processing, distribution, and storage.

3d Mapping Of Stiffness Evolution Of Hardening Concrete With Saps Using Elastic Wave Tomography

Phenomena occurring during the delicate phase of concrete curing can have a strong influence on the final mechanical properties. Therefore, monitoring the changes during hydration can provide meaningful information on the material properties. Lately, non-destructive techniques have received great attention for monitoring earlyage concrete. This paper aims to assess the ultrasonic velocity and stiffness development of hardening concrete during various curing stages using the Elastic Wave Tomography (EWT) technique. The monitoring of the curing process starts as soon as seven hours after casting and lasts up to seventy hours. A three-dimensional (3D) wave velocity map is created, and conventional concrete is compared to concrete containing SuperAbsorbent Polymers (SAPs), a novel admixture used for shrinkage mitigation by providing internal curing in the cementitious matrix. However, SAPs have been proven to reduce compressive strength when added to concrete, due to the increased porosity they induce. This porosity may also result in the reduction of the wave velocity. However, the internal curing provided by the SAPs can promote the formation of new hydration products in the matrix and thus (partly) compensate for the strength loss making the uniformity of the internal curing another important point of concern. EWT can provide global information on the homogeneity of the influence of the SAPs in the cementitious matrix as opposed to the wave velocity of a single wave path that is usually examined in practice. In addition, the determination of the early-age stiffness evolution can be connected to the final mechanical properties of the material. The dynamic Young’s Modulus is calculated and compared to the static Young’s modulus at 28 days. Predictions towards the static Young’s modulus are also made, showing good agreement.

New insights of emerald geographic origin determination based on the infrared spectroscopy of D2O and HDO molecules

Emerald geographic origin determination in laboratories typically relies on the microscopic features of inclusions, ultraviolet through visible and near-infrared (UV-Vis-NIR) spectroscopy, and trace element chemistry measured by laser ablation–inductively coupled plasma–mass spectrometry (LA-ICP-MS). Infrared (IR) spectroscopy, a non-destructive technique, has played a minor role in this process. This work conducts a systematic examination of emerald samples from twelve origins, encompassing spectroscopic, and trace element analyses. For the first time, IR absorptions related to D2O and HDO molecules within the 2600–2850cm–1 range for from multiple origins (333 emerald samples) were recorded, assigned, and classified. These IR absorptions, controlled by the concentrations of deuterium (D) and alkali metals (primarily sodium) within channels, reveal three distinct patterns and derived four groups that serve as strong evidence for origin determination. Emeralds from Zambia group display the most prevalent HDO-dominant IR-pattern I characterized by the pronounced absorption peak of Type II HDO at 2672cm–1; Panjshir (Afghanistan) and Swat (Pakistan) emeralds exhibit the transitional type IR-Pattern II with overall five peaks and obvious 2808cm–1 peak; D2O-dominant IR-Pattern III is featured by marked absorption band at 2740cm–1, and can be further subdivided into two subtypes (IIIa and IIIb). IR-Pattern IIIb is currently only found in Nigerian emeralds, characterized by strong broad band of Type I D2O due to enriched deuterium and obvious absorption of Type I HDO at 2685cm–1 due to depleted sodium. Furthermore, this research updates the classification of UV-Vis-NIR spectra, unveils the trace element features, and presents effective compositional diagrams. This contribution enriches and expands existing techniques and provides new insights for emerald origin determination.

Automated surface crack detection with inductive thermography

In recent years, inductive thermography has become established as a non-destructive inspection technique for detecting surface cracks in metallic parts. A short inductive heating pulse (0.1 – 1 s) is applied to the sample and an infrared camera records the surface temperature during and after the heating pulse. Since a surface crack obstructs both the eddy current flow and the heat flow, the defect becomes well visible in the infrared images. The technique has the advantage of being contact-free, fast and it can be fully automated. Additionally, it also provides information about the crack depth, because the deeper the crack, the greater the obstacle to the eddy current and to the heat flow. In order to detect the cracks in the infrared images of castings and forgings with complex geometry, a comparison method has been developed. The images of the inspected parts are compared with images of reference parts, and in this way artefacts caused by edges and corners of the parts are filtered out and not marked as defects. This greatly increases the robustness of the fully automated inspection, which can test parts 24 hours a day, and 7 days a week, without any human interaction, and the results of each part are well documented and stored.

How to combine soil and plant indicators to manage nitrogen fertilisation in vineyards?

ContextAlthough grapevine nitrogen (N) needs are moderate, N fertilisation in vineyards requires carefully management because berry development, aromatic composition and hence winemaking are largely influenced by N nutrition. Our objective was to develop a method for reasoned N fertilisation between and during the seasons. MethodsSpecific sensitivities of soil and plant indicators to N supply and availability were estimated and indicator robustness was quantified using contrasted environmental conditions, crop management and N fertilisation in an experimental network comprising five vineyards in southern France over four successive years. The effects of four N fertilisation treatments combining contrasted amounts, forms and timing on the indicators were tested using linear models. Multiple factorial multiple analysis was used to study the effect of environmental and management factors on indicator sensitivity to mineral N fertilisation at budburst. ResultsYield, soil mineral nitrogen at budburst, chlorophyll concentration (SPAD) at veraison, leaf nitrogen content and yeast assimilable nitrogen (YAN) at harvest, were all sensitive to N fertilisation after four years. Different effects of the N treatments were observed from year three for SPAD and from year one for YAN. Additionally, SPAD and YAN indicators distinguished contrasted strategies based on different timings of N supply or the form of N fertiliser. Lastly, the response of the SPAD and YAN indicators was only slightly influenced by pedoclimatic conditions or cultural practices, at least for the variables tested here. ConclusionTwo N indicators measured from veraison (SPAD) to harvest (YAN), can provide valuable information for tactical and long-term fertilisation decisions. Notably, early SPAD information may improve technical vineyard management of interactions between fertilisation and other practices (e.g. weed control or tillage) that interfere with plant N nutrition and enable later correction of YAN values. Combining these indicators with non-destructive imaging techniques should improve N and mineral monitoring in general.

Dendritic copper nanostructures for ultrasensitive detection of rhodamine 6G and Congo red

Surface-Enhanced Raman Scattering (SERS) is a fast-responding and non-destructive ultra-sensitive detection technique that can replace traditional chromatographic and spectroscopic analysis techniques. In this experiment, dendritic copper nanostructures were prepared by the solid-state ionics method and the vacuum hot evaporation process with an applied current of I=9μA. Using them as a SERS substrate, Raman detection was performed on the dye molecules Rhodamine 6G (R6G) and Congo Red (CR) to determine SERS enhancement performance. It is found that the growth of the prepared copper nanostructures has a top-end dominant phenomenon. The fractal dimension of the copper nanostructure was calculated using fractal theory, and it was revealed to be 1.560. Scanning electron microscopy (SEM) results showed that a large number of 60-80 nm regularly arranged copper nanoparticles were attached to the surface of the dendritic copper nanostructure. This is beneficial to increase the surface roughness of the substrate and improve the detection level of dye molecules. Subsequently, copper nanostructures can sensitively detect R6G and CR solutions as low as 1×10-13 mol/L and 1×10-7 mol/L, reaching single-molecule levels. The Raman mapping experiment showed that the mean relative standard deviation (RSD) values of R6G and CR were 12.3% and 12.9%, indicating high uniformity and reproducibility of the structure. This structure effectively achieves the fusion of uniformity, repeatability, and sensitivity, and shows broad application prospects in food detection.

Defect focused Harris3D & boundary fine-tuning optimized region growing: Lithium battery pole piece defect segmentation

As a vital component of lithium battery, the pole piece is prone to defects during the production process, which adversely affects performance and even causes safety accidents. Therefore, conducting quality inspections for lithium battery pole pieces is crucial. In this research, an optimized region growing algorithm based on 3D point cloud data is proposed to automatically detect pole piece defects. Specifically, a potential defect-focused Harris3D method (DFHarris3D) for keypoint detection and a boundary fine-tuning algorithm (BFT) are introduced. Comparative experimental results prove that our method outperforms others in terms of completeness, accuracy, and robustness. Therefore, this novel non-destructive detection technique demonstrates the potential to accurately segment surface defects in lithium battery pole pieces, which can be integrated into smart factories for fully automated defect detection.

Failure behavior study of EB-PVD TBCs under CMAS corrosion and thermal shock cycles

Abstract Calcia-magnesia-alumino-silicate (CMAS) erosion has become a major obstacle, limiting the operating temperature and service life of Thermal barrier coatings (TBCs) in aircraft engines. Constructing simulation environments that replicate TBCs’ working conditions and exploring online, non-destructive detection techniques are reliable approaches to studying coatings’ failure, representing both a global research hotspot and a challenge in this field. The paper presents an initial endeavor to establish a simulation experiment for TBCs in aviation-engine within a CMAS environment. Experimental results show that electron beam physical vapor deposition (EB-PVD) Y2O3-stabilized ZrO2 (YSZ), one of the mainstream TBCs technologies, produced 20% surface spallation after 50 thermal-shock cycles under simulated CMAS corrosion conditions. Testing and analysis of the macroscopic and microscopic structures of the failed samples, combined with SEM, EDS, and XRD findings, revealed significant physical and chemical interactions between the ceramic layer and CMAS deposits, as well as phase transformation within the coatings, leading to substantial alterations in mechanical properties and ultimately causing the failure of EB-PVD YSZ.

Advanced detection of foreign objects in fresh-cut vegetables using YOLOv5

The presence of foreign objects in fresh-cut vegetables has become a significant concern for the industry in recent years. Ensuring the safety of consumers and suppliers necessitates comprehensive precautionary measures. This study introduces a novel approach for detecting foreign objects in fresh-cut vegetables using YOLOv5. The results indicate that YOLOv5s excels in identifying foreign objects with a high detection accuracy of 98.30%, a rapid inference time of 2.6 ms, and a compact model size of 13.3 MB. The model effectively identified foreign objects such as transparent and colored plastic, paper, wood, stone, insects, glass, and metal. Moreover, YOLOv5s accurately detected foreign objects with color similarities to green onions, which is particularly challenging due to their wide color variation. Hence, the foreign objects dataset was tested and generated 98.63% and 98.67% for cabbage and green onion, respectively. Additionally, YOLOv5s successfully detected small foreign objects (2–3 mm) in two types of fresh-cut vegetables. Despite its excellent performance, the YOLOv5s model struggles to identify foreign objects that overlap with vegetable samples. To address this issue, installing an automatic conveyor unit could facilitate continuous sample movement, while a feeder unit could reduce the possibility of overlap. This research demonstrates the feasibility of implementing the YOLOv5s, as a non-destructive technique for detecting foreign objects in fresh-cut vegetables. The findings contribute to the development of an accurate, fast, and efficient real-time inspection system, with potential applications in the fresh-cut vegetable industry to enhance product quality and safety.

Fluorescence spectroscopy combined with multilayer perceptron deep learning to identify the authenticity of monofloral honey—Rape honey

Honey authenticity is critical to honey quality. The development of a quick, easy, and non-destructive technique for determining the authenticity of honey encourages an improvement in honey quality. Here, the authenticity of monofloral honey—rape honey was determined using fluorescence spectroscopy combined with multilayer perceptron (MLP) deep learning, without the need for any prior feature extraction or pre-processing. A total of 91 raw fluorescence intensity data of the real and adulterated honey samples at a fixed excitation wavelength of 280 nm were first matrixed, and all data were then categorized into a training set, a validation set, and a test set with numbers of 64, 16, and 11, respectively. The connection with dropout was selected to build and link the MLP internal network. The activation function, learning rate, optimizer, and number of epochs were among the hyperparameters of the MLP neural network that were tuned. A good MLP deep learning network model for determining the authenticity of monofloral honey, rape honey, was developed after constant validation and debugging. According to the accuracy curve of the MLP model, the accuracy of the training set increased with the number of epochs and eventually converged to 100 %, while the accuracy of the validation set could be well stabilized at about 100 % after 5000 epochs. Finally, the accuracy of the MLP model on the test set was close to 100 %. According to our findings, the MLP neural network and fluorescence intensity have great potential applications in identifying the authenticity of honey.

The Role of Near-Infrared Spectroscopy in Food Quality Assurance: A Review of the Past Two Decades

During food quality control, NIR technology enables the rapid and non-destructive determination of the typical quality characteristics of food categories, their origin, and the detection of potential counterfeits. Over the past 20 years, the NIR results for a variety of food groups—including meat and meat products, milk and milk products, baked goods, pasta, honey, vegetables, fruits, and luxury items like coffee, tea, and chocolate—have been compiled. This review aims to give a broad overview of the NIRS processes that have been used thus far to assist researchers employing non-destructive techniques in comparing their findings with earlier data and determining new research directions.

Data-driven AI for the automated classification of the isothermal heat-treated thermal barrier coatings using pulsed infrared thermography

Abstract Thermal barrier coating (TBC) is a multilayer coating applied to metallic structures exposed to high temperatures, such as aero-engine parts and gas turbine blades. These thermally insulating materials are used to extend the component life by minimizing the high-temperature exposure of structural components. As a result of the high-temperature oxidation tests, a thermally grown oxide (TGO) layer formed at the interface between the ceramic topcoat layer and the bond coat layer. The primary cause of TBC performance decline and structure failure is the growth of the oxide layer. Hence, it is crucial to regularly monitor the formation of the oxide layer and the degradation of coatings. This can be achieved by developing coating-life prediction models, which are vital for quality control, health monitoring of TBCs, maintenance decision-making, and the prevention of catastrophic failures. Existing methods for life estimation, which are dependent on empirical methods and microstructural analysis, often fail due to various material compositions and service conditions. This study proposes an automated classification of iso-thermal heat-treated TBC samples using temperature data, which helps in predicting the TBC life and monitoring the TBC degradation. TBC-coated samples are isothermal heat-treated at 1000 °C, and the initial growth of TGO is monitored using a non-destructive thermal imaging technique. Identifying distinctive features in each class corresponding to layer thickness measurement and service hours is challenging and time-consuming. So, we integrate data-driven AI models and feature extraction techniques to interpret complex thermal patterns. The performance of deep learning models and classical machine learning models demonstrates that our method can achieve maximum classification accuracy and F1-score of 89.2% and 89.0%, respectively. This automated classification framework promises a powerful tool for predictive maintenance and remaining useful lifetime estimation of hot section components reliant on TBCs.

Combining NIR spectroscopy with chemometrics for discriminating naturally ripened banana and calcium carbide ripened banana

Calcium carbide is prohibited as a fruit ripening agent in many countries due to its harmful effects. Current methods for detecting calcium carbide in fruit involve time-consuming and destructive chemical analysis techniques, necessitating the need for non-destructive and rapid detection techniques. This study combined near infrared (NIR) spectroscopy with chemometrics to detect two banana varieties ripened with calcium carbide in different forms when they are peeled or unpeeled. Sixteen linear discriminant analysis (LDA) models were developed with high average classification accuracies for classifying banana based on the mode used to ripen banana, type of carbide treatment and the duration of soaking banana in carbide solution. Banana colour was predicted with partial least squared regression (PLSR) models with R2CV > 0.74, RMSECV and <5.4 and RPD close to 3. NIR coupled with chemometrics has good potential as a technique for detecting carbide ripened banana even if the banana is peeled or not.

Ductility and cracking behavior of UHPC-RC composite beam under bending test based on acoustic emission parameters

Ultrahigh-performance concrete (UHPC) possesses improved mechanical properties due to its high strength, durability, and toughness. Currently, there is a single method for detecting the cracking behavior and evaluating the ductility of the UHPC-reinforced concrete (RC) composite beam. As an important part of structural non-destructive monitoring techniques, the acoustic emission (AE) technique can be used to analyze the damage pattern of the structure by the variation characteristics of its individual parameters. On this basis, four-point bending tests were carried out on the RC beam and UHPC-RC composite beam, and the AE technique was used for ductility analysis and cracking behavior monitoring. The results showed that the UHPC material enhanced the flexural capacity of the UHPC-RC composite beam. The AE ring counts and energy parameters could be used to analyze the cracking process of the UHPC-RC composite beam during flexural damage. The proposed new ductility indices, AE ring counts ductility index (ARD) and AE energy ductility index (AED), differed from the displacement ductility indices within the range of 2 %, providing a new method for calculating ductility indices of concrete structures. During the failure stage, the UHPC-RC composite beam produced more tensile cracks at the early stage of cracking and the stage of damage compared to the RC beam. In addition, the b value of the RC beam had a large range of fluctuations during the failure stage, indicating that the RC beam produced more micro-cracks and macro-cracks and more damage compared to the UHPC-RC composite beam.

Estimation of the soluble solid content of citrus based on the fractional-order derivative and optimal band combination algorithm.

The soluble solid content (SSC) is a primary characteristic index for evaluating the internal quality of citrus fruits. The development of rapid and nondestructive SSC detection techniques can help address the current issues of postharvest quality grading in China's citrus industry. In this study, a total of 261 experimental samples, including 70 Murcott, 91 Clementine, and 100 Navel orange, were divided into prediction and validation sets in a 7:3 ratio. After obtaining the reflection spectra and SSCs, SNV-FOD (Standard Normal Variate-Fractional-Order Derivative) was used to process the spectra, and the optimal band combination algorithm was introduced to select SSC-sensitive bands. Then, the obtained optimal dual-band combination was input into eight regression models for comparison, and the best performing models stacked ensemble models was selected. Finally, the H-ELR (HyperOpt-optimized ensemble learning regression) model, optimized using a Bayesian function, was applied for the effective estimation of SSC for three common citrus varieties in Guangxi, Murcott, Clementine, and Navel oranges. The results show that (1) the SNV-FOD preprocessing method proposed in this study improved the correlation coefficient with the SSC from 0.546 to 0.836 compared to that of the original spectrum, (2) the optimal dual-band combination (969 and 1069nm) constructed by integrating the differential index and 1.2-order derivative yielded the most accurate results (RPD=2.13), and (3) the H-ELR model, based on HyperOpt optimization, achieved good estimated performance (RPD=2.46). PRACTICAL APPLICATION: This research contributes to the development of practical SSC prediction instruments with excellent universality and ease of application.