Search Results (5,166)

Remote sensing from unoccupied aerial systems (UASs) has witnessed exponential growth. The increasing use of imaging spectroscopy sensors and RGB cameras on UAS platforms demands accurate, cross-comparable multi-sensor data. Inherent errors during image capture or processing can introduce spatial offsets, diminishing spatial accuracy and hindering cross-comparison and change detection analysis. To address this, we demonstrate the use of an optical flow algorithm, eFOLKI, for co-registering imagery from two pushbroom imaging spectroscopy sensors (VNIR and NIR/SWIR) to an RGB orthomosaic. Our study focuses on two ecologically diverse vegetative sites in Tasmania, Australia. Both sites are structurally complex, posing challenging datasets for co-registration algorithms with initial georectification spatial errors of up to 9 m planimetrically. The optical flow co-registration significantly improved the spatial accuracy of the imaging spectroscopy relative to the RGB orthomosaic. After co-registration, spatial alignment errors were greatly improved, with RMSE and MAE values of less than 13 cm for the higher-spatial-resolution dataset and less than 33 cm for the lower resolution dataset, corresponding to only 2–4 pixels in both cases. These results demonstrate the efficacy of optical flow co-registration in reducing spatial discrepancies between multi-sensor UAS datasets, enhancing accuracy and alignment to enable robust environmental monitoring. Full article

Soil calcium is a vital component in plant growth and soil health. Earthworm activities impact metal distribution and speciation a lot by changing soil pH. Nevertheless, little is known about how ecological earthworm species, particularly in Terra Rossa soil, affect soil Ca speciation distribution. This research examined the effects of the activities of four different earthworm species (epigeic species Eisenia fetida (noted as EF), endogeic species Amynthas robustus (noted as AR) and Pontoscolex corethrurus (noted as PC), anecic species Amynthas aspergillum (noted as AA)) on Ca speciations (water-soluble (Ca_Ws), exchangeable (Ca_Ex), acid-soluble bound (Ca_Ac), organic-bound (Ca_Or), and residual (Ca_Re)), soil pH, the release contents of exchangeable cations (Ca, Mg, K, and Na), total calcium (Ca_Total) contents, total nitrogen (TN) contents, soil organic carbon (SOC) concentrations, cation-exchange capacity (CEC), and NIRS spectral characteristics in Terra Rossa soil for 40 days under lab conditions. In contrast to control soil, 108.3%, 158.3%, 91.7%, and 125.0% of Ca_Ws contents in casts and 116.6%, 108.3%, 58.3% and 91.6% of Ca_Ws in uningested soil increased significantly with the inoculation of EF, PC, AR, and AA, respectively. In addition, compared with control, for casts, the contents of exchangeable Ca, Mg, K, CEC, and available-K were significantly increased in the presence of EF, PC, AR, and AA, respectively. In the casts of EF, PC, AR, and AA, soil pH values declined by 0.72, 0.80, 0.45, and 0.60 units relative to control soil, while they decreased by 0.65, 0.84, 0.34, and 0.59 units in uningested soil. The soil inoculated with PC had higher soil pH values and Ca_Ws contents than those with the other three earthworm species. Principal component analysis revealed significant differences in soil pH, Ca speciation, NIR spectra, and exchangeable base cation release between casts and uningested soil in treatments with EF, PC, AR, and AA inoculation. These findings expand, for the first time, to the ecological functions of earthworm species, especially for PC, demonstrating a capacity to alter soil Ca speciation, decrease soil pH, affect the exchangeable base cations’ release, and participate in and regulate the geochemical circulation processes in limestone regions. Full article

The sensory quality of meat, encompassing the traits of appearance, texture, and flavour, is essential to consumer acceptance. Conventional quality assessment techniques, such as instrumental methods and trained sensory panels, often face limitations due to their destructive and time-consuming nature. In recent years, optical sensing techniques have emerged as a fast, non-invasive, and non-destructive technique for the prediction of quality attributes in meat and meat products, achieving prediction accuracies of over 90%. This review critically examines the potential of optical sensing techniques, such as near-infrared spectroscopy (NIRS), Raman spectroscopy, and hyperspectral imaging (HSI), to inform about the sensory attributes of red meat, aligning with industrial demands for early information on the predicted sensory performance of inventory to support meeting consumer requirements. Recent trends and the remaining challenges associated with these techniques will be described. While technical issues related to spectral data acquisition and data processing are important challenges when considering industrial implementation, overall, optical sensing techniques, in tandem with recent developments in digitalisation and data analytics, provide potential for the online prediction of meat sensory quality in the meat processing industries. Establishing technologies for enhanced information on the product and improved possibilities for quality control will help the industry to meet consumer demands for a consistent quality of product. Full article

Visible near-infrared (Vis-NIR) spectroscopy is widely used for rapid soil element detection, but calibration models are often limited by instrument-specific constraints, including varying laboratory conditions and sensor configurations. To address this, we propose a novel calibration transfer method that eliminates the conventional requirement of designating ‘master’ and ‘slave’ devices. Instead, spectral data from two spectrometers are fused to create a master spectrum, while independent spectral data serve as slave spectra. We developed an ensemble stacking model, incorporating partial least squares regression (PLSR), support vector regression (SVR), and ridge regression (Ridge) in the first layer, with BoostForest (BF) as the second layer, trained on the fused master spectrum. This model was further integrated with three calibration transfer methods: direct standardization (DS), piecewise direct standardization (PDS), and spectral space transfer (SST), to enable seamless application across slave spectra. Applied to soil total nitrogen (TN) detection, the method achieved an R²_P of 0.842, RMSEP of 0.017, and RPD of 2.544 on the first slave spectrometer, and an R²_P of 0.830, RMSEP of 0.018, and RPD of 2.452 on the second. These results demonstrate the method’s ability to simplify calibration processes while enhancing cross-instrument prediction accuracy, supporting robust and generalizable cross-instrument applications. Full article

Multimodal brain–computer interfaces (BCIs) that combine electrical features from electroencephalography (EEG) and hemodynamic features from functional near-infrared spectroscopy (fNIRS) have the potential to improve performance. In this paper, we propose a multimodal EEG- and fNIRS-based BCI system with soft robotic (BCI-SR) components for personalized stroke rehabilitation. We propose a novel method of personalizing rehabilitation by aligning each patient’s specific abilities with the treatment options available. We collected 160 single trials of motor imagery using the multimodal BCI from 10 healthy participants. We identified a confounding effect of respiration in the fNIRS signal data collected. Hence, we propose to incorporate a breathing sensor to synchronize motor imagery (MI) cues with the participant’s respiratory cycle. We found that implementing this respiration synchronization (RS) resulted in less dispersed readings of oxyhemoglobin (HbO). We then conducted a clinical trial on the personalized multimodal BCI-SR for stroke rehabilitation. Four chronic stroke patients were recruited to undergo 6 weeks of rehabilitation, three times per week, whereby the primary outcome was measured using upper-extremity Fugl-Meyer Motor Assessment (FMA) and Action Research Arm Test (ARAT) scores on weeks 0, 6, and 12. The results showed a striking coherence in the activation patterns in EEG and fNIRS across all patients. In addition, FMA and ARAT scores were significantly improved on week 12 relative to the pre-trial baseline, with mean gains of 8.75 ± 1.84 and 5.25 ± 2.17, respectively (mean ± SEM). These improvements were all better than the Standard Arm Therapy and BCI-SR group when retrospectively compared to previous clinical trials. These results suggest that personalizing the rehabilitation treatment leads to improved BCI performance compared to standard BCI-SR, and synchronizing motor imagery cues to respiration increased the consistency of HbO levels, leading to better motor imagery performance. These results showed that the proposed multimodal BCI-SR holds promise to better engage stroke patients and promote neuroplasticity for better motor improvements. Full article

In the pharmaceutical industry, freeze-drying is crucial for the stability of active pharmaceutical ingredients (APIs). Monitoring this complex process presents challenges as traditional methods often lack real-time insights, potentially leading to quality issues and batch rejections. Effective monitoring is then essential for optimizing process parameters and minimizing waste, thus saving costs and resources. This study evaluated the application of Near-Infrared (NIR) spectroscopy for the real-time monitoring of the freeze-drying process: NIR spectra were acquired in-line via a specially designed flange in the freeze-dryer. Two approaches were investigated. The first involved freeze-drying monitoring using control charts, thus creating a reference model based on cycles under normal conditions. A PCA model was developed using these reference cycles, and an intentional fault cycle was performed to test the system’s ability to detect deviations. Multivariate control charts, utilizing Hotelling’s T² and DModX metrics, were shown to effectively monitor process deviations, enhancing the understanding of freeze-drying dynamics. The second approach was based on the use of NIR spectroscopy for assessing residual moisture during lyophilization. By integrating Partial Least Squares (PLS) regression with in-line NIR spectra, we estimated endpoints and detected faults in both reference and faulty cycles. The results showed strong correlations between PLS estimates and the Pirani–Baratron method, highlighting NIR’s applicability for monitoring drying phases. This research advocates for broader NIR implementation in pharmaceutical development, emphasizing its potential to monitor the process, ensure quality, and reduce out-of-specification product. Full article

Research was conducted on the role of brown algae as a capping agent in the synthesis of ZnO nanoparticles, the characteristics of ZnO nanoparticles, and the effect of the addition of ZnO nanoparticles and/or silane compounds on antibacterial and antifungal activities. The synthesis of ZnO nanoparticles involved green synthesis, and then nanoparticles were characterized using UV/VIS/NIR, ATR-FTIR, XRD, PSA, and SEM-EDS, followed by the in situ deposition of ZnO nanoparticles on cotton fabrics and the addition of silane compounds. The characterization of modified and unmodified cotton fabrics and antibacterial and antifungal activity tests were carried out using the disc diffusion method through measurements of the diameter of the inhibition zone against Pseudomonas aeruginosa, Staphylococcus epidermidis, and Malassezia furfur. The characterization of ZnO nanoparticles showed absorption at a wavelength of 357 nm; the number of waves was 450 cm⁻¹; the diffraction peak occurred at an angle of 36.14°; the crystal size was 15.35 nm; there was a heterogeneous particle distribution; the particle size was in the ranges of 1.74–706 nm (PSA) and 45–297 nm (SEM); and an irregular particle shape was noted. The results showed that the best antibacterial and antifungal activity was obtained in cotton + HDTMS + ZnO nanoparticles (K8) and cotton + ZnO nanoparticles+HDTMS/MTMS (K4). Full article

Ciprofloxacin (CIP), a widely used broad-spectrum antibiotic, poses a serious threat to human health and environmental safety due to its residues. The complementary monomers molecularly imprinted electrochemiluminescence sensor (MIECLS) based on a polyvinylpyrrolidone-functionalized copper nanowires (CuNWs@PVP) luminescent probe was constructed for the ultra-sensitive detection of CIP. CuNWs with low cost and high conductivity exhibited near-infrared electrochemiluminescence (NIR ECL) properties, yet their self-aggregation and oxidation led to a weakened emission phenomenon. PVP with solvent affinity and large skeleton was in situ attached to CuNWs surface to avoid CuNWs sedimentation and aggregation, and self-enhanced ECL signals were achieved. The bifunctional monomers molecularly imprinted polymer (MIP) possessed complementary active centers that increased their affinity with CIP, enhancing the accurate and sensitive detection of the target substances. The linear range of CIP using MIECLS was 5.00 × 10⁻⁹–5.00 × 10⁻⁵ mol L⁻¹ with a low limit of detection (LOD) of 2.59 × 10⁻⁹ mol L⁻¹, while the recovery rates of CIP in the spiking recovery experiment were 84.39% to 92.48%. The combination of bifunctional monomer MIP and NIR copper-based nano-luminescent probe provides a new method for the detection of CIP in food. Full article

The Oxidative Stability Index (OSI) is crucial for evaluating the commercial, nutritional, and sensory properties of extra virgin olive oils (EVOO). Near-infrared spectroscopy (NIRS) offers a rapid and cost-effective alternative to evaluate OSI with respect to traditional methods like Rancimat. This study aimed to develop a robust global NIRS model for predicting OSI in EVOO and compare it with specific models for key Spanish cultivars such as ‘Picual’, ‘Arbequina’, and ‘Sikitita’ (a new, recently released cultivar for commercial hedgerow planting systems). Using NIRS spectra from 1100 to 2500 nm, we analyzed 939 samples globally and developed cultivar-specific models based on 59 ‘Picual’, 84 ‘Arbequina’, and 48 ‘Sikitita’ samples. Partial Least Squares (PLS) regression models demonstrated promising results in all sample sets tested, with the global model outperforming individual yearly models, highlighting the importance of incorporating variability to enhance predictive performance. Log-transformed OSI data improved accuracy across all models. Additionally, discriminant analysis (LDA) was performed on NIRS spectra from five cultivars (‘Arbequina,’ ‘Picual,’ ‘Koroneiki,’ ‘Sikitita,’ and ‘Arbosana’), a total of 254 samples, achieving 96% accuracy in differentiating monovarietal EVOO samples. These findings demonstrate the versatility of NIRS for OSI modeling and cultivar discrimination, making it a valuable tool for breeding programs and quality assessment. Full article

Breast cancer remains a leading cause of cancer-related deaths among women, emphasizing the critical need for early detection and monitoring techniques. Conventional imaging modalities such as mammography, MRI, and ultrasound have face sensitivity, specificity, cost, and patient comfort limitations. This study introduces a handheld Near-Infrared Diffuse Optical Tomography (NIR DOT) probe for breast cancer imaging. The NIRscan probe utilizes multi-wavelength light-emitting diodes (LEDs) and a linear charge-coupled device (CCD) sensor to acquire real-time optical data, reconstructing cross-sectional images of breast tissue based on scattering and absorption coefficients. With wavelengths optimized for the differential optical properties of tissue components, the probe enables functional imaging, distinguishing between healthy and malignant tissues. Clinical evaluations have demonstrated its potential for precise tumor localization and monitoring therapeutic responses, achieving a sensitivity of 94.7% and specificity of 84.2%. By incorporating machine learning algorithms and a modified diffusion equation (MDE), the system enhances the accuracy and speed of image reconstruction, supporting rapid, non-invasive diagnostics. This development represents a significant step forward in portable, cost-effective solutions for breast cancer detection, with potential applications in low-resource settings and diverse clinical environments. Full article

From learning environments to battlefields to marketing teams, the desire to measure cognition and cognitive fatigue in real time has been a grand challenge in optimizing human performance. Near-infrared spectroscopy (NIRS) is an effective optical technique for measuring changes in subdermal hemodynamics, and it has been championed as a more practical method for monitoring brain function compared to MRI. This study reports on an innovative functional NIRS (fNIRS) sensor that integrates the entire system into a compact and wearable device, enabling long-term monitoring of patients. The device provides unrestricted mobility to the user with a Bluetooth connection for settings configuration and data transmission. A connected device, such as a smartphone or laptop equipped with the appropriate interface software, collects raw data, then stores and generates real-time analyses. Tests confirm the sensor is sensitive to oxy- and deoxy-hemoglobin changes on the forehead region, which indicate neuronal activity and provide information for brain activity monitoring studies. Full article

Soil organic matter (SOM) and total nitrogen (TN) are critical indicators for assessing soil fertility. Although laboratory chemical analysis methods can accurately measure their contents, these techniques are time-consuming and labor-intensive. Spectral technology, characterized by its high sensitivity and convenience, has been increasingly integrated with machine learning algorithms for soil nutrient monitoring. However, the process of spectral data analysis remains complex and requires further optimization for simplicity and efficiency to improve prediction accuracy. This study proposes a novel model to enhance the accuracy of SOM and TN predictions in northeast China’s black soil. Visible/Shortwave Near-Infrared Spectroscopy (Vis/SW-NIRS) data within the 350–1070 nm range were collected, preprocessed, and dimensionality-reduced. The scores of the first nine principal components after a partial least squares (PLS) dimensionality reduction were selected as inputs, and the measured SOM and TN contents were used as outputs to build a back-propagation neural network (BPNN) model. The results show that spectral data processed by the combination of standard normal variate (SNV) and multiple scattering correction (MSC) have the best modeling performance. To improve the accuracy and stability of this model, three algorithms named random search (RS), grid search (GS), and Bayesian optimization (BO) were introduced. The results demonstrate that Vis/SW-NIRS provides reliable predictions of SOM and TN contents, with the PLS-RS-BPNN model achieving the best performance (R² = 0.980 and 0.972, RMSE = 1.004 and 0.006 for SOM and TN, respectively). Compared to traditional models such as random forests (RF), one-dimensional convolutional neural networks (1D-CNNs), and extreme gradient boosting (XGBoost), the proposed PLS-RS-BPNN model improves R² by 0.164–0.344 in predicting SOM and by 0.257–0.314 in predicting TN, respectively. These findings confirm the potential of Vis/SW-NIRS technology and the PLS-RS-BPNN model as effective tools for soil nutrient prediction, offering valuable insights for the application of spectral technology in sensing soil information. Full article

Ensuring the safety and quality of poultry products requires efficient detection and removal of foreign materials during processing. Hyperspectral imaging (HSI) offers a non-invasive mechanism to capture detailed spatial and spectral information, enabling the discrimination of different types of contaminants from poultry muscle and non-muscle external tissues. When integrated with advanced deep learning (DL) models, HSI systems can achieve high accuracy in detecting foreign materials. However, the high dimensionality of HSI data, the computational complexity of DL models, and the high-paced nature of poultry processing environments pose challenges for real-time implementation in industrial settings, where the speed of imaging and decision-making is critical. In this study, we address these challenges by optimizing DL inference for HSI-based foreign material detection through a combination of post-training quantization and hardware acceleration techniques. We leveraged hardware acceleration utilizing the TensorRT module for NVIDIA GPU to enhance inference speed. Additionally, we applied half-precision (called FP16) post-training quantization to reduce the precision of model parameters, decreasing memory usage and computational requirements without any loss in model accuracy. We conducted simulations using two hypothetical hyperspectral line-scan cameras to evaluate the feasibility of real-time detection in industrial conditions. The simulation results demonstrated that our optimized models could achieve inference times compatible with the line speeds of poultry processing lines between 140 and 250 birds per minute, indicating the potential for real-time deployment. Specifically, the proposed inference method, optimized through hardware acceleration and model compression, achieved reductions in inference time of up to five times compared to unoptimized, traditional GPU-based inference. In addition, it resulted in a 50% decrease in model size while maintaining high detection accuracy that was also comparable to the original model. Our findings suggest that the integration of post-training quantization and hardware acceleration is an effective strategy for overcoming the computational bottlenecks associated with DL inference on HSI data. Full article

Spaceborne instruments have an irreplaceable role in detecting fundamental vegetation features that link physical properties to ecological theory, but their success depends on our understanding of the complex dynamics that control plant spectral properties—a scale-dependent challenge. We explored differences between the warmer and cooler areas of tree canopies with a ground-based experimental layout consisting of a spectrometer and a thermal camera mounted on a portable crane that enabled synergies between thermal and spectral reflectance measurements at the fine scale. Thermal images were used to characterise the thermal status of different parts of a dense circular cluster of containerised trees, and their spectral reflectance was measured. The sensitivity of the method was found to be unaffected by complex interactions. A statistically significant difference in both reflectance in the visible (VIS), near-infrared (NIR), and shortwave infrared (SWIR) bands and absorption features related to the chlorophyll, carotenoid, and water absorption bands was found between the warmer and cooler parts of the canopy. These differences were reflected in the Photochemical Reflectance Index with values decreasing as surface temperature increases and were related to higher carotenoid content and lower Leaf Area Index (LAI) values of the warmer canopy areas. With the increasingly improving resolution of data from airborne and spaceborne visible, near-infrared, and shortwave infrared (VSWIR) imaging spectrometers and thermal infrared (TIR) instruments, the results of this study indicate the potential of synergies between thermal and spectral measurements for the purpose of more accurately assessing the complex biochemical and biophysical characteristics of vegetation canopies. Full article

Coal, as a vital global energy resource, directly impacts the efficiency of power generation and environmental protection. Thus, rapid and accurate coal quality analysis is essential to promote its clean and efficient utilization. However, combined near-infrared spectroscopy and X-ray fluorescence (NIRS-XRF) spectroscopy often suffer from the particle size effect of coal samples, resulting in unstable and inaccurate analytical outcomes. This study introduces a novel correction method combining the Segment Anything Model (SAM) for precise particle segmentation and Data-Efficient Image Transformers (DeiTs) to analyze the relationship between particle size and ash measurement errors. Microscopic images of coal samples are processed with SAM to generate binary mask images reflecting particle size characteristics. These masks are analyzed using the DeiT model with transfer learning, building an effective correction model. Experiments show a 22% reduction in standard deviation (SD) and root mean square error (RMSE), significantly enhancing ash prediction accuracy and consistency. This approach integrates cutting-edge image processing and deep learning, effectively reducing submillimeter particle size effects, improving model adaptability, and enhancing measurement reliability. It also holds potential for broader applications in analyzing complex samples, advancing automation and efficiency in online analytical systems, and driving innovation across industries. Full article