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China is a country with vast territory, but economic development and population growth have reduced the usable land resources in recent years. Therefore, reclamation by pumping and filling is carried out in eastern coastal regions of China in order to meet the needs of urbanization. However, large areas of reclaimed land need rapid drainage consolidation treatment. Based on past researches on how to improve the treatment efficiency of soft clay using vacuum preloading combined with electro-osmosis, a two-dimensional drainage plane model was proposed according to the Terzaghi and Esrig consolidation theory. However, the analytical solution using two-dimensional plane model was never involved. Current analytical solutions can't have a thorough theoretical analysis of practical engineering and give relevant guidance. Considering the smearing effect and the rectangle arrangement pattern, an analytical solution is derived to describe the behavior of pore-water and the consolidation process by using EKG (electro-kinetic geo synthetics) materials. The functions of EKG materials include drainage, electric conduction and corrosion resistance. Comparison with test results is carried out to verify the analytical solution. It is found that the measured value is larger than the applied vacuum degree because of the stacking effect of the vacuum preloading and electro-osmosis. The trends of the mean measured value and the mean analytical value processes are comparable. Therefore, the consolidation model can accurately assess the change in pore-water pressure and the consolidation process during vacuum preloading combined with electro-osmosis.

Abstract Apple moldy core is a fungus-infested disease that is extremely insidious, usually occurring inside the fruit, making it very difficult to distinguish from the exterior with the naked eye. Using VIS/NIR transmission spectroscopy, this study successfully detected moldy core apples. By combining four wavelength selection algorithms (CARS, CARS-SPA, MC-UVE, and MC-UVE-SPA) with four classifiers (SVM, ELM, KNN, and LDA-KNN), discrimination models were established for two-class and three-class classifications. MC-UVE-SPA-LDA-KNN achieved an AUC of 0.99 and an accuracy of 98.82% for two-class classification, while MC-UVE-SPA achieved an AUC of 0.99 and an accuracy of 97.64% for three-class classification. This confirms MC-UVE-SPA as an effective tool for selecting wavelengths specific to moldy core apples, facilitating precise identification and differentiation of apple states. This study advances dynamic online detection of early-stage moldy core conditions in apples, reducing post-harvest disease occurrence and preserving fruit quality effectively. Graphical Abstract

Machine learning is used widely in near-infrared spectroscopy (NIRS) for fruit qualification. However, the directly split training set used contains redundant samples, and errors may be introduced into the model. Euclidean distance-based and K-nearest neighbor-based instance selection (IS) methods are widely used to remove useless samples because of their accessibility. However, they either have high accuracy and low compression or vice versa. To compress the sample size while improving the accuracy, the least-angle regression (LAR) method was proposed for classification instance selection, and a discrimination experiment was conducted on a total of four origins of 952 apples. The sample sets were split into the raw training set and testing set; the optimal training samples were selected using the LAR-based instance selection (LARIS) method, and the four other selection methods were compared. The results showed that 26.9% of the raw training samples were selected using LARIS, and the model based on these training samples had the highest accuracy. Thus, the apple origin classification model based on LARIS can achieve the goal of high accuracy and compression and provide experimental support for the least-angle regression algorithm in classification instance selection.

A disease, known as citrus greening, is a major threat to the citrus industry. The objective of this study was to investigate the feasibility of rapid detection and improving the identification accuracy of citrus greening with visible and near-infrared spectra under spectral fusion. After we obtained the spectra of the collected citrus leaves and used the polymerase chain reaction for part of them, five types of samples were sorted out: slight, moderate, serious, nutrient deficiency, and normal. This study of spectral fusion was conducted on three levels as spectral data, characteristic, and model decision, and the identification capacity was tested using prediction samples. It was found that the effect of a least squares support vector machine model for feature-level fusion based on principal component analysis presented the best performance, while in the Lin_Kernel function; the accuracy was 100%, penalty coefficient γ was 0.09, and operation time was 0.66 s. It is better than the single spectral discriminant model. The results showed that the fusion of visible and near-infrared spectra was feasible for the nondestructive detection of citrus greening disease. This method is of great significance for the healthy development of the citrus industry, and provides important reference value for the application of spectral fusion in other fields.

Chilies undergo multiple stages from field production to reaching consumers, making them susceptible to contamination with foreign materials. Visually similar foreign materials are difficult to detect manually or using color sorting machines, which increases the risk of their presence in the market, potentially affecting consumer health. This paper aims to enhance the detection of visually similar foreign materials in chilies using hyperspectral technology, employing object detection algorithms for fast and accurate identification and localization to ensure food safety. First, the samples were scanned using a hyperspectral camera to obtain hyperspectral image information. Next, a spectral pattern recognition algorithm was used to classify the pixels in the images. Pixels belonging to the same class were assigned the same color, enhancing the visibility of foreign object targets. Finally, an object detection algorithm was employed to recognize the enhanced images and identify the presence of foreign objects. Random forest (RF), support vector machine (SVM), and minimum distance classification algorithms were used to enhance the hyperspectral images of the samples. Among them, RF algorithm showed the best performance, achieving an overall recognition accuracy of up to 86% for randomly selected pixel samples. Subsequently, the enhanced targets were identified using object detection algorithms including R-CNN, Faster R-CNN, and YoloV5. YoloV5 exhibited a recognition rate of over 96% for foreign objects, with the shortest detection time of approximately 12 ms. This study demonstrates that the combination of hyperspectral imaging technology, spectral pattern recognition techniques, and object detection algorithms can accurately and rapidly detect challenging foreign objects in chili peppers, including red stones, red plastics, red fabrics, and red paper. It provides a theoretical reference for online batch detection of chili pepper products, which is of significant importance for enhancing the overall quality of chili pepper products. Furthermore, the detection of foreign objects in similar particulate food items also holds reference value.

As photocrosslinkable materials, methacryloyl-modified hydrogels are widely used as bioinks in tissue engineering. Existing printing methods to use these hydrogels, including changing the viscosity of the material or mixing them with other printing components, have been explored, but their application has been limited due to low printing quality or high cost. In addition, the complex operation of bulky equipment restricts the application of these existing printing methods. This study presents a lightweight stereolithography-based three-dimensional (3D) bioprinting system with a smart mechanical and structural design. The developed bioprinter dimensions were 300 mm × 300 mm × 200 mm and it can be placed on a benchtop. The equipment has a mini bioink chamber to store a small amount of bioink for each printing. We systematically investigated the point-by-point curing process in the 3D bioprinting method, which can print mixed cells accurately and have good biocompatibility. Here, we provide a compact, low-cost stereolithography bioprinting system with excellent biocompatibility for 3D bioprinting with methacryloyl-modified hydrogels. It can be potentially used for drug screening, studying pathological mechanisms, and constructing biological disease models.

Bruise may cause spoilage, reduce commodity economic value, and give rise to food quality and safety concerns. Therefore, it is crucial to detect whether a loquat is bruised and when it is bruised to save storage and transportation costs. At present, the bruise of loquats is mainly discriminated by the operator’s naked eye, which is affected by personal habits, light intensity, and subjective psychological factors. The detection method is time-consuming, inaccurate, inefficient, and difficult to identify the bruise’s time of loquats. Due to the fact that the color features can be used to perform the conditions of the darkened and brownish regions in bruise’s loquats, the combined spectral information and the color features method is proposed to accurately detect the storage time of mild bruise’s loquats in this study. In order to reduce economic losses, different methods are used to deal with the loquats at the corresponding bruise’s time. Loquats with four types of bruise’s time, including 6, 12, 24, and 36 h, are studied. Models with four types of characteristics, including spectral information, RGB features combined with spectral information, HSI features combined with spectral information, and mixed color features combined with spectral information (mixed-spectral), are established based on linear discriminant analysis (LDA), support vector machine (SVM), and least-squares support vector machine (LS-SVM). The investigated 400 independent samples with four bruise’s time conditions are utilized to assess the classification ability of the proposed methods. The results indicate that the Mixed-RBF-LS-SVM model has the lowest errors, and the accuracies of storage time of mild bruise’s loquats at 6, 12, 24, and 36 h are 100%, 92%, 92%, and 100%, respectively. The overall accuracy of the LS-SVM model based on mixed-spectral is 96%, and it demonstrates that the combined spectral information and color features method can be used to accurately detect the bruise’s time of loquats. Finally, the LS-SVM model based on mixed-spectral is optimized by UVE, SPA, CARS, and GA, respectively; it is found that the UVE-LS-SVM model based on mixed-spectral is the best, and the overall accuracy is 92%. It also lays a foundation for future studies about detecting the bruise’s time of fruits with a high-precision, rapid, and nondestructive measurement.

Soluble solids content (SSC) is a vital evaluation index for the internal quality of apples, and NIR spectroscopy is the preferred technique for predicting the SSC of apples. Due to the differences in fruits’ sizes, their SSC prediction models have poor robustness and low prediction accuracy, so it is important to eliminate the effects brought by the differences in fruit sizes to improve the accuracy of fruit sorting models. The NIR spectra of apples with different fruit sizes were collected by applying NIR spectroscopy online detection device, and after various preprocessing of the spectra, the partial least squares (PLS) models of apple SSC were established, respectively, and then the modeling set in the apple fruit size group of 75 mm–85 mm was used to predict the prediction set samples in the apple fruit size group of 65 mm–75 mm and 85 mm–95 mm, respectively. To better address the effects of apple size differences, data fusion techniques were used to perform an intermediate fusion of apple fruit diameter and spectra, firstly, the competitive adaptive reweighting algorithm (CARS) and the continuous projection algorithm (SPA) were used to select spectral variables and build their prediction models for apple SSC, respectively, and the results showed that the models built with 61 spectral variables selected by CARS had better performance, greatly reduced the amount of data involved in modeling, effectively simplified the model, and improved the stability of the model. The apple size variables were added to the wavelength variables selected by CARS, and the data were normalized to establish a PLS model of apple SSC with the normalized spectral and apple fruit diameter data, and the results showed that the size compensation model based on intermediate fusion had the best prediction performance, with the prediction set Rp of 0.886 for fruit diameter of 65 mm–75 mm, RMSEP of 0.536%, and its prediction set Rp was 0.913 and RMSEP was 0.497% for the fruit diameter of 85 mm–95 mm. Therefore, adding the fruit diameter variable to establish the size-compensated model of apple SSC can improve the prediction performance of the model.

Objective: Walnuts have rich nutritional value and are favored by the majority of consumers. As walnuts are shelled nuts, they are prone to suffer from defects such as mildew during storage. The fullness and mildew of the fruit impose effects on the quality of the walnuts. Therefore, it is of great economic significance to carry out a study on the rapid, non-destructive detection of walnut quality. Methods: Terahertz spectroscopy, with wavelengths between infrared and electromagnetic waves, has unique detection advantages. In this paper, the rapid and nondestructive detection of walnut mildew and fullness based on terahertz spectroscopy is carried out using the emerging terahertz transmission spectroscopy imaging technology. First, the normal walnuts and mildewed walnuts are identified and analyzed. At the same time, the image processing is carried out on the physical samples with different kernel sizes to calculate the fullness of the walnut kernels. The THz image of the walnuts is collected to extract the spectral information in different regions of interest. Four kinds of time domain signals in different regions of interest are extracted, and three qualitative discrimination models are established, including the support vector machine (SVM), random forest (RF), and k-nearest neighbor (KNN) algorithms. In addition, in order to realize the visual expression of walnut fullness, the terahertz images of the walnut are segmented with a binarization threshold, and the walnut fullness is calculated by the proportion of the shell and kernel pixels. Results: In the frequency domain signal, the amplitude intensity from high to low is the mildew sample, walnut kernel, and walnut shell, and the distinction between walnut kernel, shell samples, and mildew samples is high. The overall identification accuracy of the aforementioned three models is 90.83%, 97.38%, and 97.87%, respectively. Among them, KNN has the best qualitative discrimination effect. In a single category, the recognition accuracy of the model for the walnut kernel, walnut shell, mildew sample, and reference group (background) reaches 94%, 100%, 97.43%, and 100%, respectively. The terahertz transmission images of the five categories of walnut samples with different kernel sizes are processed to visualize the detection of kernel fullness inside walnuts, and the errors are less than 5% compared to the actual fullness of walnuts. Conclusion: This study illustrates that terahertz spectroscopy detection can achieve the detection of walnut mildew, and terahertz imaging technology can realize the visual expression and fullness calculation of walnut kernels. Terahertz spectroscopy and imaging provides a non-destructive detection method for walnut quality, which can provide a reference for the quality detection of other dried nuts with shells, thus having significant practical value.

Rails play a vital role in the bearing and guidance of high-speed trains, and the normal condition of rail components is the guarantee of the operation and maintenance safety. Fasteners are critical components for fixing the rails, so it is particularly important to detect whether they are in a normal state or not. The current rail-fastener detection models have some drawbacks, including poor generalization ability, large model volume and low detection efficiency. In view of this, an improved YoLoX-Nano rail-fastener-defect-detection method is proposed in this paper. The CA attention mechanism is added to the three output feature maps of CSPDarknet and the enhanced feature extraction part of the Path Aggregation Feature Pyramid Network (PAFPN); the Adaptively Spatial Feature Fusion (ASFF) is added after the PAFPN output feature map, which enables the semantic information of the high-level features and the fine-grained features of the bottom layer to be further enhanced. The improved YoLoX-Nano model has improved the AP value by 27.42% on fractured fasteners, 15.88% on displacement fasteners and 12.96% on normal fasteners. Moreover, the mAP value is improved by 18.75%, and it is 14.75% higher than the two-stage model Faster-RCNN on mAP. In addition, compared with YoLov7-tiny, the improved YoLoX-Nano model achieves 13.56% improvement on mAP. Although the improved model increases a certain amount of calculation, the detection speed of the improved model has been increased by 30.54 fps and by 32.33 fps when compared with that of the Single-Shot Multi-Box Detector (SSD) model and the You Only Look Once v3 (YoLov3) model, reaching 54.35 fps. The improved YoLoX-Nano model enables accurate and rapid identification of the defects of rail fasteners, which can meet the needs of real-time detection. Furthermore, it has advantages in lightweight deployment of terminals for rail-fastener detection, thus providing some reference for image recognition and detection in other fields.

Bruising is one of the main problems in the post-harvest grading and processing of ‘Zaozhong 6’ loquats, reducing the economic value of loquats, and even food quality and safety problems are caused by it. Therefore, one of the main tasks in the post-harvest processing of loquats is to detect whether loquats are bruised, as well as the degree of bruising of loquats, to reduce the loss by proper treatment. An appropriate dimensionality reduction method can be used to reduce the redundancy of variables and improve the detection speed. The multispectral analysis method (MAM) has the advantage of accurate, rapid, and nondestructive detection, which was proposed to identify the different bruising degrees of loquats in this study. Firstly, the visible and near-infrared region (Vis–NIR, 400–1000 nm), the visible region (Vis, 400–780 nm), and the near-infrared region (NIR, 781–1000 nm) were analyzed using principal component analysis (PCA) to obtain the spectral regions and PC vectors, which could be used to effectively distinguish bruised loquats from normal loquats. Then, based on the selected second PC (PC2) score images, a morphological segmentation method (MSM) was proposed to distinguish bruised loquats from normal loquats. Furthermore, the weight coefficients of corresponding wavelength points of different degrees of bruising of loquats were analyzed, and the local extreme points and both sides of the interval were selected as the characteristic wavelength points for multi-spectral image processing. A gray level co-occurrence matrix (GLCM) was used to extract texture features and gray information from two-band ratio images K782/999. Finally, the MAM was proposed to detect the degree of bruising of loquats, which included the spectral data of three characteristic wavelength points in the NIR region coupled with texture features of the two-band ratio images, and the classification accuracy was 91.3%. This study shows that the MAM can be used as an effective dimensionality reduction method. The method not only improves the effect of prediction but also simplifies the process of prediction and ensures the accuracy of classification. The MSM can be used for rapid detection of normal and bruised fruits, and the MAM can be used to classify the degree of bruising of bruised fruits. Consequently, the processed methods are effective and can be used for the rapid and nondestructive detection of the degree of bruising of fruit.

The transmission spectrum of apples is affected by the fruit’s size, which leads to poor prediction performance of the soluble solids content (SSC) models built for their different apple sizes. In this paper, three sets of near infrared (NIR) spectra of apples with various apple diameters were collected by applying NIR spectroscopy detection equipment to compare the spectra differences among various apple diameter groups. The NIR spectra of apples were corrected by studying the extinction rates within different apples. The corrected spectra were used to develop a partial least squares prediction model for their soluble solids content. Compared with the prediction model of the soluble solids content of apples without size correction, the Rp of PLSR improved from 0.769 to 0.869 and RMSEP declined from 0.990 to 0.721 in the small fruit diameter group; the Rp of PLSR improved from 0.787 to 0.932 and RMSEP declined from 0.878 to 0.531 in the large fruit diameter group. The proposed apple spectra correction method is effective and can be used to reduce the influence of sample diameter on NIR spectra.

The nondestructive discrimination model based on near-infrared is usually established by detected spectra and chemometric methods. However, the inherent differences between instruments prevent the model from being used universally, and calibration transfer is often used to solve these problems. Standard-sample calibration transfer requires additional standard samples to build a mathematical mapping between instruments. Thus, standard-free calibration transfer is a research hotspot in this field. Based on near-infrared spectroscopy (NIRS), the new combined strategy of wavelength selection and standard-free calibration transfer was proposed to transfer the model between two portable near-infrared spectrometers. Three transfer learning (TL) algorithms—transferred component analysis (TCA), balanced distribution adaptation (BDA), and manifold embedded distribution alignment (MEDA)—were applied to achieve standard-free calibration transfer. Moreover, this paper presents a relative error analysis (REA) method to select wavelength. To select the optimal model, the parameters of accuracy, precision, and recall were examined to evaluate the discriminatory capacities of each model. The findings show that the MEDA-REA model is capable of higher prediction accuracy (accuracy = 94.54%) than the other transferring models (TCA, BDA, MEDA, TCA-REA, and BDA-REA), and it is demonstrated that the new strategy has good transmission performance. Moreover, REA shows the potential to filter wavebands for calibration transfer and simplify the transferable model.

It is very important for human health to supervise the use of food additives, because excessive use of food additives will cause harm to the human body, especially lead to organ failures and even cancers. Therefore, it is important to realize high-sensibility detection of benzoic acid, a widely used food additive. Based on the theory of electromagnetism, this research attempts to design a terahertz-enhanced metamaterial resonator, using a metamaterial resonator to achieve enhanced detection of benzoic acid additives by using terahertz technology. The absorption peak of the metamaterial resonator is designed to be 1.95 THz, and the effectiveness of the metamaterial resonator is verified. Firstly, the original THz spectra of benzoic acid aqueous solution samples based on metamaterial are collected. Secondly, smoothing, multivariate scattering correction (MSC), and smoothing combined with first derivative (SG + 1 D) methods are used to preprocess the spectra to study the better spectral pretreatment methods. Then, Uninformative Variable Elimination (UVE) and Competitive Adaptive Reweighted Sampling (CARS) are used to explore the optimal terahertz band selection method. Finally, Partial Least Squares (PLS) and Least square support vector machine (LS-SVM) models are established, respectively, to realize the enhanced detection of benzoic acid additives. The LS-SVM model combined with CARS has the best effect, with the correlation coefficient of prediction set (Rp) is 0.9953, the root mean square error of prediction set (RMSEP) is 7.3 × 10−6, and the limit of detection (LOD) is 2.3610 × 10−5 g/mL. The research results lay a foundation for THz spectral analysis of benzoic acid additives, so that THz technology-based detection of benzoic acid additives in food can reach requirements stipulated in the national standard. This research is of great significance for promoting the detection and analysis of trace additives in food, whose results can also serve as a reference to the detection of antibiotic residues, banned additives, and other trace substances.

Investigations were initiated to develop terahertz (THz) techniques associated with machine learning methods of generalized regression neural network (GRNN) and back-propagation neural network (BPNN) to rapidly measure benzoic acid (BA) content in wheat flour. The absorption coefficient exhibited a maximum absorption peak at 1.94 THz, which generally increased with the content of BA additive. THz spectra were transformed into orthogonal principal component analysis (PCA) scores as the input vectors of GRNN and BPNN models. The best GRNN model was achieved with three PCA scores and spread value of 0.2. Compared with the BPNN model, GRNN model to powder samples could be considered very successful for quality control of wheat flour with a correlation coefficient of prediction (rp) of 0.85 and root mean square error of prediction of 0.10%. The results suggest that THz technique association with GRNN has a significant potential to quantitatively analyse BA additive in wheat flour.

In this work, the multiferroic property of Ba-deficient BaTiO3 (001) ultrathin film is studied employing the first-principles approach. The BaTiO3 (001) ultrathin film is more energetically stable and behaves as a semiconductor relative to the (111) and (101) configurations, confirmed from the surface grand potential and electronic density of states. The electronic structures show that the O vacancy can switch the (001) film from a semi-conductor into a metal, while the Ba defect has a slight influence on the band gap, at a concentration of ~2.13%. In Ba-deficient (001) film, the spontaneous polarization pattern is changed and a spontaneous polarization parallel to the surface is observed. Furthermore, a magnetic moment is induced, and it is found to be originated from the O atoms in the supercell. Our results suggest that a strong magnetoelectric coupling occurs because the magnetic moment exhibits a 43.66% drop when the spontaneous polarization increases from 12.84 µC/cm2 to 23.99 µC/cm2 in the deficient BaTiO3 with m = 2 under the bi-axial compress stress field.

The potential of Confocal micro-Raman spectroscopy in the quantitative analysis of pesticide (Chlorpyrifos, Omethoate) residues on orange surface is investigated in this work. Quantitative analysis models were established by partial least squares (PLS) using different preprocessing methods (Smoothing, First derivative, MSC, Baseline) for pesticide residues. For pesticide residues, the higher correlation coefficients (r) is 0.972 and 0.943, the root mean square error of prediction (RMSEP) is 2.05% and 2.36%, respectively. It is therefore clear that Confocal micro-Raman spectroscopy techniques enable rapid, nondestructive and reliable measurements, so Raman spectrometry appears to be a promising tool for pesticide residues.

Variable selection is applied widely for visible-near infrared (Vis-NIR) spectroscopy analysis of internal quality in fruits. Different spectral variable selection methods were compared for online quantitative analysis of soluble solids content (SSC) in navel oranges. Moving window partial least squares (MW-PLS), Monte Carlo uninformative variables elimination (MC-UVE) and wavelet transform (WT) combined with the MC-UVE method were used to select the spectral variables and develop the calibration models of online analysis of SSC in navel oranges. The performances of these methods were compared for modeling the Vis-NIR data sets of navel orange samples. Results show that the WT-MC-UVE methods gave better calibration models with the higher correlation coefficient (r) of 0.89 and lower root mean square error of prediction (RMSEP) of 0.54 at 5 fruits per second. It concluded that Vis-NIR spectroscopy coupled with WT-MC-UVE may be a fast and effective tool for online quantitative analysis of SSC in navel oranges.