Search Results (530)

Background/Objectives: The heterogeneity of cancer disease and the frequent ineffectiveness and resistance observed with currently available treatments highlight the importance of developing new antitumor therapies. The properties of gold nanoparticles, such as their photon-energy heating, are attractive for oncology therapy; this can be effective and localized. The combination of chemotherapy and hyperthermia is promising. Our aim was to evaluate the combination therapy of photon hyperthermia with 5-fluorouracil (5-FU) both in vitro and in vivo. Methods: This study evaluated the antitumor efficacy of a combined chemo-photothermal therapy using 5-fluorouracil (5-FU) and branched gold nanoshells (BGNSs) in a colorectal cancer model. BGNSs were synthesized via a seed-mediated method and characterized by electron microscopy and UV–vis spectroscopy, revealing an average diameter of 126.3 nm and a plasmon resonance peak at 800 nm, suitable for near-infrared (NIR) photothermal applications. In vitro assays using SW620-GFP colon cancer cells demonstrated a ≥90% reduction in cell viability after 24 h of combined treatment with 5-FU and BGNS under NIR irradiation. In vivo, xenograft-bearing nude mice received weekly intratumoral administrations of the combined therapy for four weeks. The group treated with 5-FU + BGNS + NIR exhibited a final tumor volume of 0.4 mm³ on day 28, compared to 1010 mm³ in the control group, corresponding to a tumor growth inhibition (TGI) of 100.74% (p < 0.001), which indicates not only complete inhibition of tumor growth but also regression below the initial tumor volume. Thermographic imaging confirmed that localized hyperthermia reached 45 ± 0.5 °C at the tumor site. Results: These findings suggest that the combination of 5-FU and BGNS-mediated hyperthermia may offer a promising strategy for enhancing therapeutic outcomes in patients with colorectal cancer while potentially minimizing systemic toxicity. Conclusions: This study highlights the potential of integrating nanotechnology with conventional chemotherapy for more effective and targeted cancer treatment. Full article

Grains and their derivatives play a crucial role as staple foods for the global population. Identifying grains in the food chain that are free from mycotoxin contamination is essential. Researchers have explored various traditional detection methods to address this concern. However, as grain consumption becomes increasingly time-sensitive and dynamic, traditional approaches face growing limitations. In recent years, emerging techniques—particularly molecular-based vibrational spectroscopy methods such as visible–near-infrared (Vis–NIR), near-infrared (NIR), Raman, mid-infrared (MIR) spectroscopy, and hyperspectral imaging (HSI)—have been applied to assess fungal contamination in grains and their products. This review summarizes research advances and applications of vibrational spectroscopy in detecting mycotoxins in grains from 2019 to 2025. The fundamentals of their work, information acquisition characteristics and their applicability in food matrices were outlined. The findings indicate that vibrational spectroscopy techniques can serve as valuable tools for identifying fungal contamination risks during the production, transportation, and storage of grains and related products, with each technique suited to specific applications. Given the close link between grain-based foods and humans, future efforts should further enhance the practicality of vibrational spectroscopy by simultaneously optimizing spectral analysis strategies across multiple aspects, including chemometrics, model transfer, and data-driven artificial intelligence. Full article

Pesticide residues in fruits and vegetables pose a serious threat to food safety. Traditional detection methods have defects such as complex operation, high cost, and long detection time. Therefore, it is of great significance to develop rapid, non-destructive, and efficient detection technologies and equipment. In recent years, the combination of spectroscopic techniques and imaging technologies with machine learning algorithms has developed rapidly, providing a new attempt to solve this problem. This review focuses on the research progress of the combination of spectroscopic techniques (near-infrared spectroscopy (NIRS), hyperspectral imaging technology (HSI), surface-enhanced Raman scattering (SERS), laser-induced breakdown spectroscopy (LIBS), and imaging techniques (visible light (VIS) imaging, NIRS imaging, HSI technology, terahertz imaging) with machine learning algorithms in the detection of pesticide residues in fruits and vegetables. It also explores the huge challenges faced by the application of spectroscopic and imaging technologies combined with machine learning algorithms in the intelligent perception of pesticide residues in fruits and vegetables: the performance of machine learning models requires further enhancement, the fusion of imaging and spectral data presents technical difficulties, and the commercialization of hardware devices remains underdeveloped. This review has proposed an innovative method that integrates spectral and image data, enhancing the accuracy of pesticide residue detection through the construction of interpretable machine learning algorithms, and providing support for the intelligent sensing and analysis of agricultural and food products. Full article

Functional flours, high in bioactive compounds, have garnered increasing attention, driven by consumer demand for alternative ingredients and the nutritional limitations of wheat flour. This study explores the thermal stability of phenolic compounds in various functional flours using visible, near and shortwave-infrared (Vis–NIR–SWIR) spectroscopy (350–2500 nm), integrated with machine learning (ML) algorithms. Random Forest models were employed to classify samples based on flour type, baking temperature, and phenolic concentration. The full spectral range yielded high classification accuracy (0.98, 0.98, and 0.99, respectively), and an explainability framework revealed the wavelengths most relevant for each class. To address concerns regarding color as a confounding factor, a targeted spectral refinement was implemented by sequentially excluding the visible region. Models trained on the 1000–2500 nm and 1400–2500 nm ranges showed minor reductions in accuracy, suggesting that classification is not solely driven by visible characteristics. Results indicated that legume and wheat flours retain higher total phenolic content (TPC) under mild thermal conditions, whereas grape seed flour (GSF) and olive stone flour (OSF) exhibited notable thermal stability of TPC even at elevated temperatures. These first findings suggest that the proposed non-destructive spectroscopic approach enables rapid classification and quality assessment of functional flours, supporting future applications in precision food formulation and quality control. Full article

Early and accurate detection of mechanical damage in prunes is crucial for preserving postharvest quality and enabling automated sorting. This study proposes a practical and reproducible method for identifying cumulative bruising in prunes using visible–near-infrared (Vis–NIR) reflectance spectroscopy coupled with machine learning techniques. A self-developed impact simulation device was designed to induce progressive damage under controlled energy levels, simulating realistic postharvest handling conditions. Spectral data were collected from the equatorial region of each fruit and processed using a hybrid modeling framework comprising continuous wavelet transform (CWT) for spectral enhancement, uninformative variable elimination (UVE) for optimal wavelength selection, and support vector machine (SVM) for classification. The proposed CWT-UVE-SVM model achieved an overall classification accuracy of 93.22%, successfully distinguishing intact, mildly bruised, and cumulatively damaged samples. Notably, the results revealed nonlinear reflectance variations in the near-infrared region associated with repeated low-energy impacts, highlighting the capacity of spectral response patterns to capture progressive physiological changes. This research not only advances nondestructive detection methods for prune grading but also provides a scalable modeling strategy for cumulative mechanical damage assessment in soft horticultural products. Full article

The quality of wine largely depends on the quality of wine grapes, which is determined by their chemical composition. Therefore, measuring parameters related to grape ripeness, such as soluble solids content (SSC), is crucial for harvesting high-quality grapes. Visible–Near-Infrared (Vis/NIR) spectroscopy enables effective, non-destructive detection of SSC in grapes. However, commercial Vis/NIR spectrometers are often expensive, bulky, and power-consuming, making them unsuitable for on-site applications. This article integrated the AS7265X sensor to develop a low-cost handheld IoT multispectral detection device, which can collect 18 variables in the wavelength range of 410–940 nm. The data can be sent in real time to the cloud configuration, where it can be backed up and visualized. After simultaneously removing outliers detected by both Monte Carlo (MC) and principal component analysis (PCA) methods from the raw spectra, the SSC prediction model was established, resulting in an ${\mathrm{R}}_V^2$ of 0.697. Eight preprocessing methods were compared, among which moving average smoothing (MAS) and Savitzky–Golay smoothing (SGS) improved the ${\mathrm{R}}_V^2$ to 0.756 and 0.766, respectively. Subsequently, feature wavelengths were selected using UVE and SPA, reducing the number of variables from 18 to 5 and 6, respectively, further increasing the ${\mathrm{R}}_V^2$ to 0.809 and 0.795. The results indicate that spectral data optimization methods are effective and essential for improving the performance of SSC prediction models. The IoT Vis/NIR Spectroscopic System proposed in this study offers a miniaturized, low-cost, and practical solution for SSC detection in wine grapes. Full article

This study assessed the applicability of visible–near-infrared (vis-NIR) spectroscopy to predicting the water retention characteristics of forest soils in Japan, which vary widely owing to the presence of volcanic ash. Soil samples were collected from 34 sites, and the volumetric water content was measured at eight levels of matric suction. Spectral data were processed by using the second derivative of the absorbance, and regression models were developed by using explainable boosting machine (EBM), which is an interpretable machine learning method. Although the prediction accuracy was limited owing to the small sample size and soil heterogeneity, EBM performed better under saturated conditions (R² = 0.30), which suggests that vis-NIR spectroscopy can capture water-related features, especially under wet conditions. Importance analysis consistently selected wavelengths that were associated with organic matter and hydrated clay minerals. The important wavelengths clearly shifted from free-water bands in wet soils to mineral-related absorption bands in dry soils. These findings highlight the potential of coupling vis-NIR spectroscopy with interpretable models like EBM for estimating the hydraulic properties of forest soils. Improved accuracy is expected with larger datasets and stratified models by soil type, which can facilitate more efficient soil monitoring in forests. Full article

In this review, we aim to provide a comprehensive overview of the roles of fungi in horticultural crops. Their beneficial roles and pathogenic effects are investigated. In addition, the recent advancements in fungal detection and management strategies (especially the use of spectral analysis) are summarized. Beneficial fungi, including plant growth-promoting fungi (PGPF), ectomycorrhizal fungi (ECM), and arbuscular mycorrhizal fungi (AMF), enhance nutrient uptake, promote root and shoot development, improve photosynthetic efficiency, and support plant resilience against biotic and abiotic stresses. Additionally, beneficial fungi contribute to flowering, seed germination, and disease management through biofertilizers, microbial pesticides, and mycoinsecticides. Conversely, pathogenic fungi cause significant diseases affecting roots, stems, leaves, flowers, and fruits, leading to crop yield losses. Advanced spectral analysis techniques, such as Fourier Transform Infrared Spectroscopy (FTIR), Near-Infrared Spectroscopy (NIR), Raman, and Visible and Near-Infrared Spectroscopy (Vis-NIR), alongside traditional methods like Polymerase Chain Reaction (PCR) and Enzyme-Linked Immunosorbent Assay (ELISA), have shown promise in detecting and managing fungal pathogens. Emerging applications of fungi in sustainable agriculture, including biofertilizers and eco-friendly pest management, are discussed, underscoring their potential to enhance crop productivity and mitigate environmental impacts. This review provides a comprehensive understanding of the complex roles of fungi in horticulture and explores innovative detection and management strategies. Full article

Background/Objective: Thrombosis is the leading cause of graft failure and immediate hysterectomy following uterus transplantation (UTx). Currently, there is no standardized method for real-time assessment of UTx graft perfusion. This feasibility study aims to evaluate the utility of a near-infrared spectroscopy (NIRS) probe for non-invasive monitoring of local cervical tissue oxygenation (StO₂) during UTx. As proof-of-concept for the NIRS device, cervical StO₂ was also measured during non-donor hysterectomy and bilateral salpingo-oophorectomy to establish its capacity to reflect perfusion changes corresponding to vascular ligation. Methods: The ViOptix T. Ox Tissue Oximeter NIRS probe was attached to four uterine cervices during hysterectomy procedures and three separate donor cervices during UTx. Real-time StO₂ measurements were recorded at critical surgical steps: baseline, ovarian vessel ligation, contralateral ovarian vessel ligation, uterine vessel ligation, contralateral uterine vessel ligation, and colpotomy for hysterectomy; donor internal iliac vein anastomosis to recipient external iliac vein, donor internal iliac artery anastomosis to recipient external iliac artery, contralateral donor internal iliac vein anastomosis to recipient external iliac vein, contralateral donor internal iliac artery anastomosis to recipient external iliac artery, and donor and recipient vagina anastomosis for UTx. Results: During hysterectomy, average StO₂ levels sequentially decreased: 70.2% (baseline), 56.7% (ovarian vessel ligation), 62.1% (contralateral ovarian vessel ligation), 50.5% (uterine vessel ligation), 35.8% (contralateral uterine vessel ligation), and 8.5% (colpotomy). Conversely, during UTx, StO₂ progressive increased with each anastomosis: 8.9% (internal iliac vein- external iliac vein), 27.9% (internal iliac artery-external iliac artery), 56.9% (contralateral internal iliac vein-contralateral external iliac vein), 65.9% (contralateral internal iliac artery-contralateral external iliac artery), and 65.2% (vaginal anastomosis). Conclusions: The inverse correlation between StO₂ and vascular ligation during hysterectomy and the progressive rise in StO₂ during UTx suggests that cervical tissue oximetry may serve as a non-invasive modality for monitoring uterine graft perfusion. Further studies are warranted to determine whether these devices complement current assessments of uterine graft viability and salvage thrombosed grafts. Full article

Visible and near-infrared (Vis–NIR) spectroscopy enables the rapid prediction of soil properties but faces three limitations with conventional machine learning: information loss and overfitting from high-dimensional spectral features; inadequate modeling of nonlinear soil–spectra relationships; and failure to integrate multi-scale spatial features. To address these challenges, we propose ReSE-AP Net, a multi-scale attention residual network with spatial pyramid pooling. Built on convolutional residual blocks, the model incorporates a squeeze-and-excitation channel attention mechanism to recalibrate feature weights and an atrous spatial pyramid pooling (ASPP) module to extract multi-resolution spectral features. This architecture synergistically represents weak absorption peaks (400–1000 nm) and broad spectral bands (1000–2500 nm), overcoming single-scale modeling limitations. Validation on the LUCAS2009 dataset demonstrated that ReSE-AP Net outperformed conventional machine learning by improving the R² by 2.8–36.5% and reducing the RMSE by 14.2–69.2%. Compared with existing deep learning methods, it increased the R² by 0.4–25.5% for clay, silt, sand, organic carbon, calcium carbonate, and phosphorus predictions, and decreased the RMSE by 0.7–39.0%. Our contributions include statistical analysis of LUCAS2009 spectra, identification of conventional method limitations, development of the ReSE-AP Net model, ablation studies, and comprehensive comparisons with alternative approaches. Full article

A method based on Vis-NIR spectroscopy and machine learning-based modeling for non-destructive detection of the internal disorders of black flesh, spongy tissue, jelly seed, and soft nose in mango fruit was developed using the vis-NIR spectra of intact mango fruit of three cultivars sourced from three orchards in each of the two seasons, with spectra collected both at harvest and after storage. After spectra were acquired of the stored fruit, the fruit cheeks were cut longitudinally to allow visual assessment of the incidence of the internal disorders. Five models were evaluated: two tree-based algorithms (J48 and random forest), one neural network (multilayer perceptron, MLP), and two SVM training algorithms (sequential minimal optimization, SMO, and LibSVM). The models were evaluated using a tenfold cross-validation approach. Non-destructive discrimination of health from all disordered and healthy fruit from fruit with specific disorders was achieved with an accuracy ranging from 72.3 to 97.0% when using spectra collected at harvest and 63.7 to 96.2% when using spectra collected after ripening. No one machine learning algorithm out-performed other methods—for spectra collected at harvest, the highest discrimination accuracy was achieved with RF and MLP for black flesh, J48 for spongy tissue, and LibSVM for soft nose and jelly seed. For spectra collected of stored fruit, the highest discrimination accuracy was achieved with SMO for jelly seed and RF for soft nose. A recommendation is made for the consideration of ensemble models in future. The ability to predict the development of the disorder using spectra of at-harvest fruit offers the potential to guide postharvest practices and reduce incidence of internal disorders in mangoes. Full article

Soil organic carbon (SOC) is a critical indicator of soil health and carbon sequestration potential. Accurate, efficient, and scalable SOC estimation is essential for sustainable orchard management and climate-resilient agriculture. However, traditional visible–near-infrared (Vis–NIR) spectroscopy often suffers from limited chemical specificity and weak adaptability in heterogeneous soil environments. To overcome these limitations, this study develops a five-stage modeling framework that systematically integrates Fourier Transform Infrared (FTIR) spectroscopy with hybrid machine learning techniques for non-destructive SOC prediction in citrus orchard soils. The proposed framework includes (1) FTIR spectral acquisition; (2) a comparative evaluation of nine spectral preprocessing techniques; (3) dimensionality reduction via three representative feature selection algorithms, namely the Successive Projections Algorithm (SPA), Competitive Adaptive Reweighted Sampling (CARS), and Principal Component Analysis (PCA); (4) regression modeling using six machine learning algorithms, namely the Random Forest (RF), Support Vector Regression (SVR), Gray Wolf Optimized SVR (SVR-GWO), Partial Least Squares Regression (PLSR), Principal Component Regression (PCR), and the Back-propagation Neural Network (BPNN); and (5) comprehensive performance assessments and the identification of the optimal modeling pathway. The results showed that second-derivative (SD) preprocessing significantly enhanced the spectral signal-to-noise ratio. Among feature selection methods, the SPA reduced over 300 spectral bands to 10 informative wavelengths, enabling efficient modeling with minimal information loss. The SD + SPA + RF pipeline achieved the highest prediction performance (R² = 0.84, RMSE = 4.67 g/kg, and RPD = 2.51), outperforming the PLSR and BPNN models. This study presents a reproducible and scalable FTIR-based modeling strategy for SOC estimation in orchard soils. Its adaptive preprocessing, effective variable selection, and ensemble learning integration offer a robust solution for real-time, cost-effective, and transferable carbon monitoring, advancing precision soil sensing in orchard ecosystems. Full article

Milk jujube has become an increasingly popular tropical fruit. The sugar content, which is commonly represented by the soluble solid content (SSC), is a key indicator of the flavor, internal quality, and market value of milk jujubes. Traditional methods for assessing SSC are time-consuming, labor-intensive, and destructive. These methods fail to meet the practical demands of the fruit market. A rapid, stable, and effective non-destructive detection method based on visible/near-infrared (Vis/NIR) spectroscopy is proposed here. A Vis/NIR reflectance spectroscopy system covering 340–1031 nm was constructed to detect SSC in milk jujubes. A structured spectral modeling framework was adopted, consisting of outlier elimination, dataset partitioning, spectral preprocessing, feature selection, and model construction. Comparative experiments were conducted at each step of the framework. Special emphasis was placed on the impact of outlier detection and dataset partitioning strategies on modeling accuracy. A data-augmentation-based unsupervised anomaly sample elimination (DAUASE) strategy was proposed to enhance the data validity. Multiple data partitioning strategies were evaluated, including random selection (RS), Kennard–Stone (KS), and SPXY methods. The KS method achieved the best preservation of the original data distribution, improving the model generalization. Several spectral preprocessing and feature selection methods were used to enhance the modeling performance. Regression models, including support vector regression (SVR), partial least squares regression (PLSR), multiple linear regression (MLR), and backpropagation neural network (BP), were compared. Based on a comprehensive analysis of the above results, the DAUASE + KS + SG + SNV + CARS + SVR model exhibited the highest prediction performance. Specifically, it achieved an average precision (AP_p) of 99.042% on the prediction set, a high coefficient of determination (R_P²) of 0.976, and a low root-mean-square error of prediction (RMSEP) of 0.153. These results indicate that Vis/NIR spectroscopy is highly effective and reliable for the rapid and non-destructive detection of SSC in milk jujubes, and it may also provide a theoretical basis for the practical application of rapid and non-destructive detection in milk jujubes and other jujube varieties. Full article

Tea is one of the most widely consumed non-alcoholic beverages globally, yet its yield and quality are significantly impacted by herbivory from tea geometrids. To accurately detect herbivory stress in tea leaves, this study integrated metabolomics with visible-near-infrared spectroscopy (VIS-NIRS) to explore its in situ capabilities and underlying mechanisms. The results demonstrated that metabolomic data, combined with PCA-based linear dimensionality reduction, could effectively distinguish between tea leaves subjected to herbivory by different densities of tea geometrids. VIS-NIRS successfully identified herbivore-damaged leaves, achieving an optimal average classification accuracy of 0.857. Furthermore, VIS-NIRS was able to differentiate leaves subjected to herbivory on different days. The application of appropriate preprocessing techniques significantly enhanced temporal classification, achieving the highest average classification accuracy of 0.773. By integrating metabolomics and spectral band analysis, the spectral range of 800–2500 nm was found to more accurately identify leaves exposed to herbivory for a prolonged period. Compared to using the full spectrum, the model built within this wavelength range improved classification accuracy by 10%. In conclusion, this study provides a solid theoretical foundation for the in situ, rapid detection of tea geometrid herbivory stress in the field using VIS-NIRS, offering key technical support for future applications. Full article

This study investigated the optical properties (OPs) and Monte Carlo (MC) simulations of light propagation in Healthy Group (HG) and Blackhearted Group (BG) potatoes. The MC simulation of light propagation indicated that both the photon packet weight and the penetration depth were significantly lower in blackhearted tissues than in healthy tissues. The simulation revealed deeper light penetration in healthy tissues than in the blackhearted tissues, approximately 6.73 mm at 805 nm, whereas the penetration depth in blackhearted tissues was much shallower (1.30 mm at 805 nm). Additionally, the simulated absorption energy at both 490 nm and 805 nm was higher in blackhearted tissues, suggesting that these wavelengths effectively detect blackheart in potatoes. The absorption (μ_a) and reduced scattering (μ’_s) coefficients were obtained using Vis-NIR spectroscopy, which represented a notable increase in μ_a in BH tissues, particularly around 550–850 nm, and an increase in μ’_s across the Vis-NIR region. Based on transmittance (Tt), μ_a and μ’_s, Support Vector Machine Discriminant Analysis (SVM-DA) models demonstrated exceptional performance, achieving 95.83–100.00% accuracy in Cross-Validation sets, thereby confirming the robustness and reliability of the optical features for accurate blackheart detection. These findings provide valuable theoretical insights into the accuracy and robustness of predictive models for detecting blackhearted potatoes. Full article