Search Results (107)

The early detection of internal damage caused by Elasmopalpus lignosellus in fresh asparagus constitutes a challenge for the agro-export industry due to the limited sensitivity of traditional visual inspection. This study evaluated the potential of VIS–NIR hyperspectral imaging (390–1036 nm) combined with machine-learning models to discriminate between infested (PB) and sound (SB) asparagus spears. A balanced dataset of 900 samples was acquired, and preprocessing was performed using Savitzky–Golay and SNV. Four classifiers (SVM, MLP, Elastic Net, and XGBoost) were compared. The optimized SVM model achieved the best results (CV Accuracy = 0.9889; AUC = 0.9997). The spectrum was reduced to 60 bands while LOBO and RFE were used to maintain high performance. In external validation (n = 3000), the model achieved an accuracy of 97.9% and an AUC of 0.9976. The results demonstrate the viability of implementing non-destructive systems based on VIS–NIR to improve the quality control of asparagus destined for export. Full article

Maize is a globally major crop; however, the prevalence of mixed-aged seeds in the market complicates consumer selection and impedes the healthy development of the maize industry. This study introduces a novel method for identifying maize seeds of different storage ages. Seeds were categorized into three age groups: new seeds, one-year stored, and two-year stored, with 300 seeds per group. Hyperspectral images of all 900 samples were acquired using a visible and near-infrared (Vis-NIR) hyperspectral imaging system. To achieve optimal results with minimal spectral data, a feature wavelength selection algorithm based on Inter-Class Feature Differences (IFD) was proposed. When only using the selected three key wavelengths, combined with the linear discriminant analysis (LDA) algorithm, the discrimination accuracy among three different age groups reached 85.67%, while the discrimination accuracy between new and aged seeds achieved 95.33%. Compared to two commonly used variable selection algorithms—Successive Projections Algorithm (SPA) and Random Frog (RF), the proposed IFD method demonstrated superior performance when only a limited number of key wavelengths were used for modeling. These results indicate that the proposed algorithm offers an effective and efficient solution for maize seed age discrimination, showing great potential for practical application. Full article

Background: Scabies, caused by Sarcoptes scabiei var. hominis, remains difficult to confirm histologically when parasites are sparse or fragmented. Conventional microscopy is particular but limited by small sample size, tissue destruction, and observer dependence. Objective: To evaluate visible–near-infrared hyperspectral imaging (VIS–NIR HSI) as a label-free optical method for detecting S. scabiei in human skin sections and to assess its compatibility with routine HE staining. Methods: Formalin-fixed, paraffin-embedded (FFPE) skin tissue from six patients with histologically verified scabies was analysed using VIS–NIR HSI (500–1000 nm). Unstained sections mounted on CaF₂ substrates and parallel HE-stained slides were imaged. Spectral datasets were processed by principal component analysis and segmentation to distinguish mite structures from epidermal and dermal compartments. Results: The chitin-rich mite exoskeleton exhibited a reproducible reflectance slope in the near-infrared range (R₈₅₀/R₅₅₀ > 1.5), clearly separating parasite from host tissue (R₈₅₀/R₅₅₀ < 1.0). PCA confirmed consistent cluster separation across all cases (ΔPC ≈ 3.7 ± 0.2). These contrasts remained detectable in HE-stained sections, validating applicability to conventional slides. Conclusions: VIS–NIR HSI enables reliable, label-free detection of S. scabiei mites in both unstained and HE-stained human skin tissue. By combining morphological and biochemical information in a single modality, HSI represents a promising adjunct to digital dermatopathology and may improve diagnostic sensitivity in challenging or atypical cases. Full article

Nondestructive quality detection of characteristic fruits is essential for ensuring nutritional value, economic viability, and consumer safety in global supply chains, yet traditional destructive methods compromise sample integrity and scalability. Visible and near-infrared (Vis/NIR) spectroscopy offers a transformative solution by enabling rapid, non-invasive multi-attribute quantification through molecular overtone vibrations. This review examines recent advancements in Vis/NIR-based fruit quality detection, encompassing fundamental principles, system configurations, and detection strategies calibrated to fruit biophysical properties. Firstly, optical mechanisms and system architectures (portable, online, vehicle-mounted) are compared, emphasizing their compatibility with fruit structural complexity. Then, critical challenges arising from fruit-specific characteristics—such as rind thickness, pit interference, and spatial heterogeneity—are analyzed, highlighting their impact on spectral accuracy. Applications across diverse fruit categories (pitted, thin-rinded, and thick-rinded) are systematically reviewed, with case studies demonstrating the robust prediction of key quality indices. Subsequently, considerations in model development and validation are presented. Finally, persistent limitations in model transferability and environmental adaptability are discussed, proposing future research directions centered on integrating hyperspectral imaging, AI-driven calibration transfer, standardized spectral databases, and miniaturized, field-deployable sensors. Collectively, these methodological breakthroughs will pave the way for autonomous, next-generation quality assessment platforms, revolutionizing postharvest management for characteristic fruits. Full article

Introduction: Oral squamous cell carcinoma (OSCC) is one of the most common tumours worldwide. This study investigated the suitability of visible and near-infrared hyperspectral imaging compared to visual assessment and conventional digital image analysis for quantifying immunohistochemical staining on the example of Dickkopf-3 (DKK-3) in OSCC. Materials and methods: A retrospective analysis of TMAs containing DKK-3 stained OSCC of 50 patients was retrieved from the archives at the Institute of Pathology, Medical University of Innsbruck. TMAs were first evaluated visually, followed by digital image analysis using QuPath (version 0.3.2, open-source software). For hyperspectral imaging, six exemplary cases were selected (three cases with strong expression and three cases with weak expression) and evaluated. The collected hyperspectral images were visualised using TIVITA (Tissue Imaging System). The resulting true-colour images and the classified HSI images were then assessed using the QuPath software. The Allred score and the H-score were used for all analyses. Results: 97 tissue cores were used for visual and digital image analysis. No significant difference was found between the evaluations of visual and digital image analysis using the H-score (pWilcoxon = 0.278), and both H-scores correlated significantly with each other (p^Spearman < 0.001). Similar results were also found using the Allred score. The kappa value was 0.67, which represents a “substantial” correlation. Finally, the H-scores and Allred scores were compared for visual, digital, and HSI imaging. No significant differences were found between the three groups concerning the H-score (pWilcoxon > 0.1). Using Cohen’s Kappa, a “fair” to “moderate” correlation was observed between the three evaluations. Conclusion: Visible and near-infrared hyperspectral imaging (VIS-NIR-HSI) is a promising complementary tool for digital pathology workflows. This proof-of-concept study suggests that HSI offers the potential for more objective quantification of DKK-3 expression in oropharyngeal squamous cell carcinoma, particularly in cases with weak staining. However, given the small sample size and exploratory design, the findings should be regarded as hypothesis-generating. Future studies with larger, clinically annotated cohorts and standardised workflows are needed before any consideration of routine clinical application. Full article

Research on packaged fruits has seen a notable upturn primarily driven by consumers’ desire for fruit safety and quality across the distribution network. This study examined the effectiveness of hyperspectral imaging (HSI) combined with chemometrics to assess the internal quality of packaged and non-packaged fresh fruits. Visible–near-infrared (Vis-NIR; 400–1000 nm) and short-wave infrared (SWIR; 1000–2500 nm) hyperspectral images of apples and plums were captured using 200 samples for each fruit across three groups—plastic wrap (PW), polyethylene terephthalate (PET) box, and non-packaged (NP)—for the prediction of soluble solid content (SSC), moisture content (MC), and pH. A partial least square regression (PLSR) model demonstrated promising results on SSC and MC across all sample groups in both Vis-NIR and SWIR, with performance ranked NP > PW > PET. Calibration and prediction coefficients of determination (R²) exceeded 0.82, 0.80, and 0.79, with root mean square errors (RMSE) less than 0.57, 0.59, and 0.59 for NP, PW, and PET, respectively. This research outcome confirmed the suitability of HSI as a critical instrument for predicting the composition of fresh fruits inside plastic packaging, offering a quick and non-invasive approach for quality evaluation in supply chains. 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

The potato is the third most important crop in the world, and more than 375 million metric tonnes of potatoes are produced globally on an annual basis. Potato Virus Y (PVY) poses a significant threat to the production of seed potatoes, resulting in economic losses and risks to food security. Current detection methods for PVY typically rely on serological assays for leaves and PCR for tubers; however, these processes are labor-intensive, time-consuming, and not scalable. In this proof-of-concept study, we propose the use of unmanned aerial vehicles (UAVs) integrated with hyperspectral cameras, including a downwelling irradiance sensor, to detect the PVY in commercial growers’ fields. We used a 400–1000 nm visible and near-infrared (Vis-NIR) hyperspectral camera and trained several standard machine learning and deep learning models with optimized hyperparameters on a curated dataset. The performance of the models is promising, with the convolutional neural network (CNN) achieving a recall of 0.831, reliably identifying the PVY-infected plants. Notably, UAV-based imaging maintained performance levels comparable to ground-based methods, supporting its practical viability. The hyperspectral camera captures a wide range of spectral bands, many of which are redundant in identifying the PVY. Our analysis identified five key spectral regions that are informative in identifying the PVY. Two of them are in the visible spectrum, two are in the near-infrared spectrum, and one is in the red-edge spectrum. This research shows that early-season PVY detection is feasible using UAV hyperspectral imaging, offering the potential to minimize economic and yield losses. It also highlights the most relevant spectral regions that carry the distinctive signatures of PVY. This research demonstrates the feasibility of early-season PVY detection using UAV hyperspectral imaging and provides guidance for developing cost-effective multispectral sensors tailored to this task. Full article

Controlling forage quality and grazing are crucial for sustainable livestock production, health, productivity, and animal performance. However, the limited availability of reliable handheld sensors for timely pasture quality prediction hinders farmers’ ability to make informed decisions. This study investigates the in-field dynamics of Mombasa grass (Megathyrsus maximus) forage biomass production and quality using optical techniques such as visible imaging and near-infrared (VIS-NIR) hyperspectral proximal sensing combined with machine learning models enhanced by covariance-based error reduction strategies. Data collection was conducted using a cellphone camera and a handheld VIS-NIR spectrometer. Feature extraction to build the dataset involved image segmentation, performed using the Mahalanobis distance algorithm, as well as spectral processing to calculate multiple vegetation indices. Machine learning models, including linear regression, LASSO, Ridge, ElasticNet, k-nearest neighbors, and decision tree algorithms, were employed for predictive analysis, achieving high accuracy with R² values ranging from 0.938 to 0.998 in predicting biomass and quality traits. A strategy to achieve high performance was implemented by using four spectral captures and computing the reflectance covariance at NIR wavelengths, accounting for the three-dimensional characteristics of the forage. These findings are expected to advance the development of AI-based tools and handheld sensors particularly suited for silvopastoral systems. Full article

(1) Background: Salt stress poses a significant challenge to plant productivity, particularly in forestry and agriculture. This research explored the physiological adaptations of Salix matsudana × alba to varying salt stress levels and assessed the utility of hyperspectral imaging (HSI) integrated with machine learning for stress detection; (2) Methods: Physiological metrics, such as photosynthesis, chlorophyll concentration, antioxidant enzyme activity, proline levels, membrane stability, and malondialdehyde (MDA) accumulation, were analyzed under controlled experimental conditions. Spectral data in the visible (Vis) and near-infrared (NIR) ranges were acquired, with preprocessing techniques enhancing data precision. The study established quantitative detection models for physiological indicators and developed a salt stress monitoring model; (3) Results: Photosynthetic efficiency and chlorophyll synthesis while elevating oxidative damage indicators, including enzyme activity, proline content, and membrane permeability. Strong correlations between spectral signatures and physiological changes highlighted HSI’s effectiveness for early stress detection. Among the machine learning models, the Convolutional Neural Network (CNN) trained on Vis+NIR data with standard normal variate (SNV) preprocessing achieved 100% classification accuracy; (4) Conclusions: The results demonstrated that HSI, coupled with modeling techniques, is a powerful non-invasive tool for real-time monitoring of salt stress, providing valuable insights for early intervention and contributing to sustainable agricultural and forestry practices. Full article

The fruit supply chain requires simple, non-destructive, and fast tools for quality evaluation both in the field and during the post-harvest phase. In this study, a portable visible and near-infrared (Vis/NIR) spectrophotometer and a portable Vis/NIR hyperspectral imaging (HSI) device were tested to highlight genetic differences among apricot cultivars, and to develop multi-cultivar and multi-year models for the most important marketable attributes (total soluble solids, TSS; titratable acidity, TA; dry matter, DM). To do this, the fruits of seventeen cultivars from a single experimental orchard harvested at the commercial maturity stage were considered. Spectral data emphasized genetic similarities and differences among the cultivars, capturing changes in the pigment content and macro components of the apricot samples. In recent years, machine learning techniques, such as artificial neural networks (ANNs), have been successfully applied to more efficiently extract valuable information from spectral data and to accurately predict quality traits. In this study, prediction models were developed based on a multilayer perceptron artificial neural network (ANN-MLP) combined with the Levenberg–Marquardt learning algorithm. Regarding the Vis/NIR spectrophotometer dataset, good predictive performances were achieved for TSS (R² = 0.855) and DM (R² = 0.857), while the performance for TA was unsatisfactory (R² = 0.681). In contrast, the optimal predictive ability was found for models of the HSI dataset (TSS: R² = 0.904; DM: R² = 0.918, TA: R² = 0.811), as confirmed by external validation. Moreover, the ANN allowed us to identify the most predictive input spectral regions for each model. The results showed the potential of Vis/NIR spectroscopy as an alternative to traditional destructive methods to monitor the qualitative traits of apricot fruits, reducing the time and costs of analyses. Full article

To enhance lychee quality assessment and address inconsistencies in post-harvest pest detection, this study presents a multi-source fusion approach combining hyperspectral imaging, X-ray imaging, and visible/near-infrared (Vis/NIR) spectroscopy. Traditional single-sensor methods are limited in detecting pest damage, particularly in lychees with complex skins, as they often fail to capture both external and internal fruit characteristics. By integrating multiple sensors, our approach overcomes these limitations, offering a more accurate and robust detection system. Significant differences were observed between pest-free and infested lychees. Pest-free lychees exhibited higher hardness, soluble sugars (11% higher in flesh, 7% higher in peel), vitamin C (50% higher in flesh, 2% higher in peel), polyphenols, anthocyanins, and ORAC values (26%, 9%, and 14% higher, respectively). The Vis/NIR data processed with SG+SNV+CARS yielded a partial least squares regression (PLSR) model with an R² of 0.82, an RMSE of 0.18, and accuracy of 89.22%. The hyperspectral model, using SG+MSC+SPA, achieved an R² of 0.69, an RMSE of 0.23, and 81.74% accuracy, while the X-ray method with support vector regression (SVR) reached an R² of 0.69, an RMSE of 0.22, and 76.25% accuracy. Through feature-level fusion, Recursive Feature Elimination with Cross-Validation (RFECV), and dimensionality reduction using PCA, we optimized hyperparameters and developed a Random Forest model. This model achieved 92.39% accuracy in pest detection, outperforming the individual methods by 3.17%, 10.25%, and 16.14%, respectively. The multi-source fusion approach also improved the overall accuracy by 4.79%, highlighting the critical role of sensor fusion in enhancing pest detection and supporting the development of automated non-destructive systems for lychee stem borer detection. Full article

Egg freshness is a critical factor that influences the egg’s nutritional value, safety, and overall quality; consequently, it is a priority for both producers and consumers. This review examines the factors that affect egg freshness, and it evaluates both traditional and modern methods for assessing egg freshness. Traditional techniques, such as the Haugh unit test and candling, have long been utilized; however, they have limitations, which are primarily due to their destructive nature. The review also highlights advanced non-destructive methods, including Vis-NIR spectroscopy, ultrasonic testing, machine vision, thermal imaging, hyperspectral imaging, Raman spectroscopy, and NMR/MRI technologies. These techniques offer rapid and accurate assessments while preserving the integrity of the eggs. Despite the current challenges related to calibration and implementation, integrating artificial intelligence (AI) and machine learning with these innovative technologies presents a promising avenue for the improvement of freshness evaluation. This development could revolutionize quality control processes in the egg industry, ensuring consistently high-quality eggs for consumers. Full article

Goji berry is acknowledged for its notable medicinal attributes and elevated free radical scavenger properties. Nevertheless, its susceptibility to mechanical injuries and biological disorders reduces the commercial diffusion of the fruit. A hyperspectral imaging system (HSI) was employed to identify common defects in the Vis-NIR range (400–1000 nm). The sensorial evaluation of visual appearance was used to obtain the reference measurement of defects. A supervised classification model employing PLS-DA was developed using raw and pre-processed spectra, followed by applying a covariance selection algorithm (CovSel). The classification model demonstrated superior performance in two classifications distinguishing between sound and defective fruit, achieving an accuracy and sensitivity of 94.9% and 96.9%, respectively. However, when extended to a more complex task of classifying fruit into four categories, the model exhibited reliable results with an accuracy and sensitivity of 74.5% and 77.9%, respectively. These results indicate that a method based on hyperspectral visible-NIR can be implemented for rapid and reliable methods of online quality inspection securing high-quality goji berries. Full article