Search Results (253)

Soil testing has long been used to optimize fertilization and crop production. More recently, soil health testing has emerged to reflect the growing interest in soil multifunctionality and ecosystem services. Soil health encompasses physical, chemical, and biological properties that support ecosystem functions and sustainable agriculture. Despite its relevance to several United Nations Sustainable Development Goals (SDGs 1, 2, 3, 6, 12, 13, and 15), comprehensive soil health testing is not widely practiced due to complexity and cost. The aim of the study presented here was to contribute to the further development, implementation, and testing of an integrated procedure for soil health assessment in practice. We developed and tested a rapid, standardized soil health assessment tool that combines near-infrared spectroscopy (NIRS) and multi-nutrient 0.01 M CaCl₂ extraction with Inductive Coupled Plasma Mass Spectroscopy analysis. The tool evaluates a wide range of soil characteristics with high accuracy (R² ≥ 0.88 for most parameters) and has been evaluated across more than 15 countries, including those in Europe, China, New Zealand, and Vietnam. The results are compiled into a soil health indicator report with tailored management advice and a five-level ABCDE score. In a Dutch test set, 6% of soils scored A (optimal), while 2% scored E (degraded). This scalable tool supports land users, agrifood industries, and policymakers in advancing sustainable soil management and evidence-based environmental policy. 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

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

The reliance on deep learning models for sensor-based material classification amplifies the demand for labeled training data. However, acquiring large-scale, annotated spectral data for applications such as near-infrared (NIR) reflectance spectroscopy in plastic sorting remains a significant challenge due to high acquisition costs and environmental variability. This paper investigates the potential of large language models (LLMs) in synthetic spectral data generation. Specifically, it examines whether LLMs have acquired sufficient implicit knowledge to assist in generating spectral data and introduce meaningful variations that enhance model performance when used for data augmentation. Classification accuracy is reported exclusively as a proxy for structural plausibility of the augmented spectra; maximizing augmentation performance itself is not the study’s goal. From as little as one empirical mean spectrum per class, LLM-guided simulation produced data that enabled up to 86% accuracy, evidence that the generated variation preserves class-distinguishing information. While the approach performs best for spectral distinct polymers, overlapping classes remain challenging. Additionally, the transfer of optimized augmentation parameters to unseen classes indicates potential for generalization across material types. While plastic sorting serves as a case study, the methodology may be applicable to other domains such as agriculture or food quality assessment, where spectral data are limited. The study outlines a novel path toward scalable, AI-supported data augmentation in spectroscopy-based classification systems. 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

Background: Despite the success of transcatheter aortic valve repair (TAVR) over the past years, its impact on global and cerebral hemodynamics remains largely unexplored. Changes in cerebral blood flow may be associated with delirium, which may occur in 26 to 29% of cases. We aimed to examine the relationships between global hemodynamic parameters and cerebral parameters in patients who underwent TAVR and their impact on postinterventional delirium. Methods: Patients scheduled for TAVR were enrolled after obtaining written informed consent. Patients received light sedation according to standard procedures. Cerebral blood flow (CBF) was measured with a noninvasive near-infrared spectroscopy-based method using intravenous indocyanine green injection. CBF measurements were taken at the beginning of the TAVR procedure and after the valve was in place. Patients were screened for delirium using CAM-ICU and NuDESC tests before and after intervention. Results: A total of 52 of 60 patients remained for analysis. Thirteen patients (25%) developed delirium. Mean arterial pressure (MAP) remained unchanged, while cardiac output increased after TAVR by 44%. CBF also increased after TAVR. No significant difference was observed in CBF changes between the groups with and without delirium. A linear mixed model analysis revealed a linear relationship between CO and CBF but not between MAP and CBF. In an exploratory analysis, decreased cerebral oxygenation and increased deoxygenated hemoglobin, as measured by NIRS after TAVR, were associated with delirium. Conclusions: The results confirm that CO is an independent factor in CBF, while CBF changes per se are not linked to delirium. However, we found a mismatch between CBF and regional cerebral parameters, which may reflect cerebral metabolism and its relation to the development of delirium. This remains to be confirmed by further studies. Full article

The objective of this study was to evaluate the feasibility of using visible, near-infrared, and short-wave infrared (VIS/NIR/SWIR) spectroscopy coupled with chemometrics for non-destructive prediction of nutritional components in Galia-type melon fruit. A total of 175 fully ripened melons were analyzed for soluble solids content (SSC), dry matter (DM), pH, and titratable acidity (TA) using partial least squares regression (PLSR), principal components regression (PCR), and multilinear regression (MLR) models. Reflectance spectra were captured at three fruit locations (pedicel, equatorial, and blossom end) in the 350–2500 nm range. The PLSR models yielded the highest accuracy, particularly for SSC (R = 0.80) and SSC/TA (R = 0.79), using equatorial zone data. Variable selection using the genetic algorithm (GA) successfully identified the spectral regions critical for each nutritional parameter at the pedicel, equatorial, and blossom end areas. Key wavelengths for SSC were found around 670–720 nm and 900–1100 nm, with important wavelengths for pH prediction located near 1450 nm, and, for dry matter, in the ranges 1900–1950 nm. Variable importance in projection (VIP) analysis confirmed that specific wavelengths between 680 and 720 nm, 900 and 1000 nm, 1400 and 1500 nm, and 1900 and 2000 nm were consistently critical in predicting the SSC, DM, and SSC/TA ratio. The highest VIP scores for SSC prediction were noted around 690 nm and 950 nm, while dry matter prediction was influenced most by wavelengths in the 1450 nm to 1950 nm range. This study demonstrates the potential of VIS/NIR/SWIR spectroscopy for rapid, non-destructive melon quality assessment, with implications for commercial postharvest management. Full article

This study aims to evaluate and classify the ripening stages of yellow-fleshed kiwifruit by integrating spectral and physicochemical data collected from the pre-harvest phase through 60 days of storage. A portable near-infrared (NIR) spectrometer (900–1700 nm) was used to develop predictive models for soluble solids content (SSC) and firmness (FF), testing multiple preprocessing methods within a Partial Least Squares Regression (PLSR) framework. SNV preprocessing achieved the best predictions for FF (R²P = 0.74, RMSEP = 12.342 ± 0.274 N), while the Raw-PLS model showed optimal performance for SSC (R²P = 0.93, RMSEP = 1.142 ± 0.022°Brix). SSC was more robustly predicted than FF, as reflected by RPD values of 2.6 and 1.7, respectively. For ripening stage classification, an Artificial Neural Network (ANN) outperformed other models, correctly classifying 97.8% of samples (R² = 0.95, RMSE = 0.08, MAE = 0.03). These results demonstrate the potential of combining NIR spectroscopy with AI techniques for non-destructive quality assessment and accurate ripeness discrimination. The integration of regression and classification models further supports the development of intelligent decision-support systems to optimize harvest timing and postharvest handling. Full article

Trypsin inhibitors (TI) are naturally occurring antinutritional factors found in soybean seeds [Glycine max. (L.)] that decrease the growth rate of livestock, causing malnutrition and digestion troubles. The current accurate method to quantify TI levels in soybean seeds or meals is by high-performance liquid chromatography (HPLC); however, it is time-consuming, creating bottlenecks in industrial processing. Establishing a near-infrared reflectance spectroscopy (NIR) model for estimating TI in seeds and meals would provide a more efficient and cost-effective method for breeding programs and feed producers. In this study, 300 soybean lines, both seeds and meals, were analyzed for TI content using HPLC, and calibration models were created based on spectral data collected from a Perten DA 7250 NIR instrument. The resulting models demonstrated robust validation, achieving accuracy rates of 97% for seed total TI, 97% for seed Kunitz TI, and 89% for meal total TI. The findings of this study are significant as no NIR calibration models had previously been developed for TI estimation in soybean seed and meal. These models can be used by breeding programs to efficiently assess their lines and by industry to quickly evaluate their soybean meal quality. Full article

The main objective of this study was to evaluate the possibilities of visible-near-infrared spectroscopy for investigating water and nitrogen stress in tomato plants. Two varieties of tomato plants (Red Bounty and Manusa) were grown in a greenhouse. Plants were divided into three groups: control, reduced nitrogen fertilization, and reduced watering. Spectral measurements of tomato leaves were made on-site. A USB4000 spectrometer for 450–1100 nm and a handheld AlbaNIR for the 900–1650 nm region were used for the spectra acquisition. Twenty-four vegetative indices were calculated using the reflectance characteristics of plants. Soft Independent Modeling of Class Analogy (SIMCA) models were developed for classification. Additionally, aquagrams were calculated. Results show differences between the spectra of leaves from control and stressed plants for both tomato varieties. Aquagrams clearly show the differences in water structures in the three groups of plants. The performance of developed SIMCA models for discriminating plants according to growing conditions was very high. The total accuracy was between 86.89% and 97.09%. Several vegetation indices successfully differentiate control and stressed plants for both tomato varieties. The results show successful differentiation of the control and stressed tomato plants based on spectral characteristics of the plants’ leaves in the visible and near-infrared region. Full article

This study investigated prefrontal cortex activity during the viewing and evaluation of pictures depicting scenarios with varying levels of danger, with a focus on the modulatory effects of personality traits and decision-making styles. The study sample included 120 male participants (44.4 ± 12.9 years) and 87 female participants (38.9 ± 10.5 years). Functional Near-Infrared Spectroscopy (fNIRS) was used to measure prefrontal oxygenation during the period of looking at pictures and the subsequent period of judging how dangerous they looked. Psychometric assessments included the Zuckerman–Kuhlman–Aluja Personality Questionnaire (ZKA-PQ) and the Melbourne Decision-Making Questionnaire (MDMQ). The results revealed significant time-by-region (F = 2.9, p = 0.013) and danger level by region interactions (F = 2.8, p = 0.021) during the viewing period. During the evaluation period, a significant time-by-region interaction was observed (F = 8.7, p < 0.001). High sensation seekers exhibited reduced oxygenation levels in specific right prefrontal regions, reflecting a differential neural response to varying danger levels. Similarly, individuals with higher Aggressiveness and Extraversion displayed distinct oxygenation patterns during the evaluation phase, suggesting that personality traits influence prefrontal activity. However, no significant effects of decision-making styles were detected in either phase. These findings emphasise the pivotal role of the prefrontal cortex in assessing scene safety and highlight how neural responses are modulated by personality traits, rather than by decision-making styles. Full article

The launch of a successful quality-oriented breeding program requires both mining the residual diversity in grain quality parameters contained in the elite, high-yielding breeding material with good agronomic performance and introgression of new germplasm, such as local landraces, with a high level of targeted quality parameters per se. This study analyzed the combining abilities of 31 maize landraces and two divergent inbred lines–testers (ZPL217 and ZPL-255/75-5) regarding the yield and protein, starch, and lipid content, assessed by Near Infrared Reflectance (NIR) spectroscopy as a fast, non-destructive, and cost-effective method. The general combining ability (GCA) defines the average behavior of genotype in hybrid combination, resulting from additive gene action, so positive GCA values of landraces AN13 and AN197 for protein, AN632 for lipids, and AN594 for starch content indicate that they can be valuable sources of the mentioned properties in quality-oriented maize breeding programs. The obtained correlation between starch content and protein and yield (−0.948 **; 0.587 **) pointed out that an increase in the protein content during breeding will be accompanied by a decrease in the starch content and yield. The specific combining ability (SCA) of the testers used, suggests their possible application in establishing and improving quality breeding programs’ initial material. Full article

A photoplethysmography (PPG) pulse in reflection mode represents the change in diffuse reflectance at the skin surface during a cardiac cycle and is commonly used in wearable devices to monitor heart rate. Commercial PPG sensors often rely on the reflectance signal from light sources at two different wavelength regions, green, such as λ = 523 nm, and near infrared (NIR), such as λ = 945 nm. Early in vivo studies of wearable sensors showed that green light is more beneficial than NIR light in optimizing PPG sensitivity. This contradicts the common trends in the standard near infrared spectroscopy techniques, which rely on the long optical pathlengths at NIR wavelengths to achieve optimal depth sensitivity. To quantitatively analyze the spectral characteristics of PPG across the wavelength region of 500–900 nm in a controlled environment, this study performs the spectral measurement of PPG signals using a simple and cost-effective optical phantom model with two distinct layers and a customized diffuse reflectance spectroscopy system. In addition, Monte Carlo simulations are used to elaborate the underlying phenomena at the green and NIR wavelengths when considering different epithelial thicknesses and source–detector distances (SDD). Full article

The early detection of bronze disease is a significant challenge not only in conservation science but also in various industrial fields that utilize copper alloys (i.e., shipbuilding and construction). Due to the aggressive nature of this corrosion pathway, developing methods for its early detection is pivotal. The presence of copper trihydroxychlorides is the main key indicator of the ongoing autocatalytic process. Commonly used for pigment identification, reflectance imaging spectroscopy (RIS) or fiber optics reflectance spectroscopy (FORS) was recently employed for mapping atacamite distribution in extended bronze corrosion patinas. In this work, we detected the onset of bronze disease using visible–near-infrared (VIS-NIR) multispectral reflectography, which allowed for disclosing features that were poorly detectable to the naked eye. The image cube was analyzed using the spectral correlation mapper (SCM) algorithm to map the distribution of copper trihydroxychlorides. FORS and Raman spectroscopy were employed to characterize the patina composition and validate RIS data. A set of bronze samples, representative of Florentine Renaissance workshops, was specifically realized for the present study and artificially aged at different corrosion stages. Full article

Accurate and timely acquisition of soil information is crucial for precision agriculture, food security, and environmental protection. Proximal visible near-infrared reflectance (vis–NIR) spectroscopy has been widely employed for rapid and accurate soil measurement, but its point measurement nature limits its direct applicability for large-scale soil surveys. On the other hand, remote sensing techniques can provide soil information at a larger scale, but their resolution is relatively coarse. While both techniques have been used independently for soil analyses, integrating vis–NIR spectroscopy with remote sensing remains a challenge and is underexplored, especially at the field scale. This study addresses this gap by combining field vis–NIR spectra with Gaofen-1 remote sensing data to spatially analyze soil organic matter and total nitrogen at the field scale. Unlike previous work, we first applied Gaofen-1 data and 10 derived spectral indices to estimate soil organic matter and total nitrogen using partial least squares regression and random forest, identifying the optimal combination of spectral indices. Then, we integrated the proximal vis–NIR spectra with this optimal spectral index combination for improved soil property estimation. This integration advanced existing methodologies by leveraging the high spatial resolution of Gaofen-1 data and the detailed spectral information from vis–NIR spectroscopy. The results showed the following: (1) the coefficient of variation across different crop growth stages of Gaofen-1 data was more crucial for modeling these two properties compared to bare soil Gaofen-1 data; (2) integrating proximal vis–NIR spectra with Gaofen-1 data improved model performance, yielding Lin’s concordance correlation coefficient (${\rho }_c$) values of 0.63 and 0.72 and ratios of performance to interquartile distance (RPIQ) of 1.99 and 1.59 for soil organic matter and total nitrogen, respectively; and (3) the combined use of vis–NIR spectra and Gaofen-1 data provided higher spatial estimation accuracy (R² of 0.68 and 0.57 for soil organic matter and total nitrogen) compared to ordinary kriging (R² of 0.63 and 0.31 for soil organic matter and total nitrogen). These results demonstrate that the synergistic use of remote sensing and proximal soil sensing is a practical approach for spatially estimating soil organic matter and total nitrogen at the field scale. Full article