Search Results (433)

Stable crop yields require an appropriate supply of essential soil nutrients such as nitrogen (N), phosphorus (P), and potassium (K) based on the accurate diagnosis of soil nutrient status. Traditional laboratory analysis of soil nutrients is often complicated and time-consuming and does not provide real-time nutrient status. Visible–near-infrared (Vis-NIR) spectroscopy has emerged as a non-destructive and rapid method for estimating soil nutrient levels. Vis-NIR spectra reflect sample characteristics as the peak intensities; however, they are often affected by various artifacts and complex variables. Since Vis-NIR spectroscopy does not directly measure nutrient levels in soil, improving estimation accuracy is essential. For spectral preprocessing, the most important aspect is to develop an appropriate preprocessing strategy based on the characteristics of the data and identify artifacts such as noise, baseline drift, and scatter in the spectral data. Machine learning-based modeling techniques such as partial least-squares regression (PLSR) and support vector machine regression (SVMR) enhance estimation accuracy by capturing complex patterns of spectral data. Therefore, this review focuses on the use of Vis-NIR spectroscopy for evaluating soil properties including soil water content, organic carbon (C), and nutrients and explores its potential for real-time field application through spectral preprocessing and machine learning algorithms. Vis-NIR spectroscopy combined with machine learning is expected to enable more efficient and site-specific nutrient management, thereby contributing to sustainable agricultural practices. Full article

Background: Eccentric cycling exercise (ECC) offers a low-metabolic-demand approach to exercise, potentially making it valuable for patients with pulmonary vascular disease (PVD). The aim of this study was to investigate how quadriceps and frontal cortex oxygenation, assessed by near-infrared spectroscopy (NIRS), differs during ECC compared to concentric cycling exercise (CON) in patients with PVD and in healthy individuals. Methods: This randomized controlled crossover trial involved patients with PVD, defined as either pulmonary arterial hypertension (PAH) or chronic thromboembolic pulmonary hypertension (CTEPH), and healthy volunteers. Participants performed both CON and ECC at identical submaximal work rates, following a stepwise incremental protocol. NIRS was used to continuously monitor tissue oxygenation and surrogates for blood volume changes in the quadriceps and frontal cortex. Results: A total of 57 participants were included, 33 PVD patients (19 with PAH and 14 with CTEPH; 13 women; mean age: 50 ± 15 years) and 24 healthy volunteers (14 women; 50 ± 14 years). In PVD patients, at end-exercise, cerebral tissue oxygenation (CTO) was significantly higher during ECC compared to CON (6.10%; 95% CI: 1.85 to 10.42; p < 0.01), whereas muscle tissue oxygenation (MTO) was similar. In healthy individuals, at end-exercise, CTO was similar during ECC and CON, whereas MTO was significantly higher (2.60%; 95% CI: 0.03 to 5.17; p = 0.047). There were no significant differences in CTO and MTO between patients with PVD and healthy individuals. Discussion: In this randomized controlled crossover trial, patients with PVD exhibited higher CTO during ECC compared to CON, which may indicate altered cerebral oxygen extraction and hemodynamic responses potentially related to impaired vascular function. In contrast, healthy individuals demonstrated higher MTO during ECC, likely reflecting improved muscular oxygen utilization and efficiency due to the mechanical and metabolic characteristics of eccentric exercise. Full article

Portable near-infrared reflectance spectroscopy (NIRS) offers the opportunity of a rapid measurement of forage dry matter concentration, allowing producers to make faster adjustments in real time. This study compared dry matter (DM) concentration predictions of three units of a portable near-infrared reflectance spectrometer (pNIRS) with conventional forced-air oven drying (48 h at 60 °C) using corn forage and silage samples. Moreover, a common on-farm method (Koster tester) was also compared. The reflectance curve used by pNIRS to predict DM was obtained by scanning WPCS samples and developed by SciO. A total of 113 whole-plant corn forage (WPCF) and 27 whole-plant corn silage (WPCS) samples from 66 corn hybrids were obtained from three separate experiments conducted between 2018 and 2019. These three experiments were completely independent of each other, with sample collections over different periods. In Experiment 1, all treatments were tested in WPCF, and the DM concentration of the forced-air oven differed from Koster testers (35.4 vs. 34.3% DM, on average, respectively) and all three pNIRS units (35.4 vs. 30.7% DM, on average, respectively), with no differences among pNIRS. All treatments were positively correlated with the forced-air oven treatment DM values. Experiment 2 evaluated the Koster tester and pNIRS in WPCF on farms, and the Koster tester differed from pNIRS (37.2 vs. 33.3% DM, on average, respectively) treatments. All pNIRS were positively correlated with Koster tester treatment. In Experiment 3, all treatments were tested in WPCS, and the DM concentration of the forced-air oven was greater than other treatments (35.3 vs. 32.8% DM, on average, respectively). Overall, Koster tester predictions for both Experiments 1 and 3 were better correlated with the forced-air oven than pNIRS. Additionally, pNIRS showed a lower mean bias, although low coefficients of determination and concordance correlation were observed in Experiment 3 compared to Experiments 1 and 2, which might be related to the prediction curve. Further calibrations of the predictive curve with forage samples would be needed to reasonably estimate the DM concentration of WPCF, whereas a greater number of samples could account for the variations in WPCS due to large heterogeneity in particle composition (e.g., leaves, stem, and kernel), size, and distribution. Full article

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

The Shenduntou Site, a significant Zhou Dynasty settlement in Anhui Province, provides rare insights into early Chinese woodcraft. This study examines exceptionally preserved wooden structures from Well J1, dating to the Western Zhou period (9th–8th c. BCE). Anatomical analysis identified the timber as Firmiana simplex (L.), indicating ancient selection of this locally available species for its water resistance and mechanical suitability in well construction. Comprehensive degradation assessment revealed severe structural deterioration: maximum water content (1100% ± 85% vs. modern 120% ± 8%) demonstrated extreme porosity from hydrolysis; X-ray diffraction (XRD) showed a 69.5% reduction in cellulose crystallinity (16.1% vs. modern 52.8%); Fourier Transform Infrared Spectroscopy (FTIR) spectroscopy confirmed near-total hemicellulose degradation, partial cellulose loss, and lignin enrichment due to chemical recalcitrance; Scanning Electron Microscopy (SEM) imaging documented multiscale damage including vessel thinning, pit membrane loss, and cell wall delamination from hydrolytic, microbial, and mineral degradation. These findings reflect Western Zhou inhabitants’ pragmatic resource utilisation while highlighting advanced material deterioration that poses significant conservation challenges, providing critical insights into Zhou-era woodcraft and human–environment interactions in the lower Yangtze region. 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

Plastic waste and pollution is growing rapidly worldwide and most plastics end up in landfill or are incinerated because high-quality recycling is not possible. Plastic-type identification with a low-cost, handheld spectral approach could help in parts of the world where high-end spectral imaging systems on conveyor belts cannot be implemented. Here, we investigate how two fundamentally different handheld infrared spectral devices can identify plastic types by benchmarking the same analysis against a high-resolution bench-top spectral approach. We used the handheld Plastic Scanner, which measures a discrete infrared spectrum using LED illumination at different wavelengths, and the SpectraPod, which has an integrated photonics chip which has varying responsivity in different channels in the near-infrared. We employ machine learning using SVM, XGBoost, Random Forest and Gaussian Naïve Bayes models on a full dataset of plastic samples of PET, HDPE, PVC, LDPE, PP and PS, with samples of varying shape, color and opacity, as measured with three different experimental approaches. The high-resolution spectral approach can obtain an accuracy (mean ± standard deviation) of (0.97 ± 0.01), whereas we obtain (0.93 ± 0.01) for the SpectraPod and (0.70 ± 0.03) for the Plastic Scanner. Differences of reflectance at subsequent wavelengths prove to be the most important features in the plastic-type classification model when using high-resolution spectroscopy, which is not possible with the other two devices. Lower accuracy for the handheld devices is caused by their limitations, as the spectral range of both devices is limited—up to 1600 nm for the SpectraPod, while the Plastic Scanner has limited sensitivity to reflectance at wavelengths of 1100 and 1350 nm, where certain plastic types show characteristic absorbance bands. We suggest that combining selective sensitivity channels (as in the SpectraPod) and illuminating the sample with varying LEDs (as with the Plastic Scanner) could increase the accuracy in plastic-type identification with a handheld device. 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