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In soil science, the allocation of soil samples to their respective origins holds paramount significance, as it serves as a crucial investigative tool. In recent times, with the increasing use of proximal sensing and advancements in machine-learning techniques, new approaches have accompanied these developments, enhancing the effectiveness of soil utilization in soil science. This study investigates soil classification based on four parent materials. For this purpose, a total of 59 soil samples were collected from 12 profiles and the vicinity of each profile at a depth of 0–30 cm. Surface soil samples were analyzed for elemental concentrations using X-Ray fluorescence (XRF) and inductively coupled plasma–optical emission spectrometry (ICP-OES) and soil spectra using a visible near-infrared (Vis-NIR) spectrometer. Soil samples collected from soil profiles (12 soil samples) and surface (47 soil samples) were used to classify parent materials using machine learning-based algorithms such as Support Vector Machine (SVM), Ensemble Subspace k-Near Neighbor (ESKNN), and Ensemble Bagged Trees (EBTs). Additionally, as a validation of the classification techniques, the dataset was subjected to five-fold cross-validation and independent sample set splitting (80% calibration and 20% validation). Evaluation metrics such as accuracy, F score, and G mean were used to evaluate prediction performance. Depending on the dataset and algorithm used, the classification success rates varied between 70% and 100%. Overall, the ESKNN (99%) produced better results than other classification methods. Additionally, Relief algorithms were employed to identify key variables for each dataset (ICP-OES: CaO, Fe2O3, Al2O3, MgO, and MnO; XRF: SiO2, CaO, Fe2O3, Al2O, and MnO; Vis-NIR: 567, 571, 572, 573, and 574 nm). Subsequent soil reclassification using these reduced variables revealed reduced accuracies using Vis-NIR data, with ESKNN still yielding the best results. [ABSTRACT FROM AUTHOR]

Cation exchange capacity is a crucial metric for managing soil fertility and promoting agricultural sustainability. An alternative technique for the non-destructive assessment of important soil parameters is reflectance spectroscopy. The main focus of this paper is on how to analyze and predict the content of various soil cation exchange capacities (CEC) in arid conditions (Sohag governorate, Egypt) at a low cost using laboratory analysis of CEC, visible near-infrared and shortwave infrared (Vis-NIR) spectroscopy, partial least-squares regression (PLSR), and Ordinary Kriging (OK). Utilizing reflectance spectroscopy with a spectral resolution of 10 nm and laboratory studies with a spectral range of 350 to 2500 nm, 104 surface soil samples were collected to a depth of 30 cm in the Sohag governorate, Egypt (which is part of the dry region of North Africa), in order to accomplish this goal. The association between the spectroradiometer and CEC averaged values was modeled using PLSR in order to map the predicted value using Ordinary Kriging (OK). Thirty-one soil samples were selected for validation. The predictive validity of the cross-validated models was evaluated using the coefficient of determination (R2), root mean square error (RMSE), residual prediction deviation (RPD), and ratio of performance to interquartile distance (RPIQ). The results indicate that ten transformation methods yielded calibration models that met the study's requirements, with R2 > 0.6, RPQ > 2.5, and RIQP > 4.05. For evaluating CEC in Vis-NIR spectra, the most efficient transformation and calibration model was the reciprocal of Log R transformation (R2 = 0.98, RMSE = 0.40, RPD = 6.99, and RIQP = 9.22). This implies that combining the reciprocal of Log R with PLSR yields the optimal model for predicting CEC values. The CEC values were best fitted by four models: spherical, exponential, Gaussian, and circular. The methodology used here does offer a "quick", inexpensive tool that can be broadly and quickly used, and it can be readily implemented again in comparable conditions in arid regions. [ABSTRACT FROM AUTHOR]

This study aimed to establish sound relationships between soil properties of agricultural land in central Spain and their spectral attributes to contribute to deriving an indicator for sustainable farm management. Sixteen plots, managed under various conditions, eight with traditional tillage and eight with other alternative managements, were selected to gather soil samples representing three predominant soil orders (Leptosols, Cambisols, and Luvisols). Soil sampling was conducted from depths ranging from 0 to 30 cm (0–50; 5–10, 10–20, and 20–30 cm), ensuring a broad spectrum of sample variability across different times and locations. The reflectance of soil 64 soil samples was measured within the range of 400 to 900 nm, and the corresponding concentrations of soil organic carbon and majoritarian minerals, calcium carbonate, quartz, phyllosilicates, K-Feldspar, and plagioclase were determined for each sample. Partial least squares analysis was employed to construct prediction models using a calibration dataset comprising 66% of randomly selected samples. The remaining 33% of samples were utilized for model validation. The prediction models for the measured soil chemical properties yielded R2 values ranging from 0.14 to 0.79. Only SOC and limestone provided accurate prediction models. These findings hold promise for developing a soil health indicator tailored for site-specific crop management. However, the complex composition of soil organic carbon and calcium carbonate in certain soils underscores the importance of careful interpretation and validation of remote sensing data, as well as the need for advanced modeling approaches that can account for the interactions between multiple soil constituents. [ABSTRACT FROM AUTHOR]

In this study, a regression model of paddy soil properties using diffuse reflectance spectroscopy was developed to replace chemical soil analysis as a more efficient alternative. Soil samples were collected and analyzed from saltwater paddy fields located in Jeongnam-myeon, Hwaseong-si, Gyeonggi-do in the Republic of Korea, and the spectral data of wet and dry soil were collected. The regression models were compared and analyzed using partial least squares regression (PLSR) with Savitzky–Golay smoothing (SG smoothing) and Standard Normal Variate (SNV) preprocessing to predict the soil properties. Analysis showed that the predictive regression model of wet soil with SG smoothing and an SNV did not meet the evaluation criteria of a fair model. However, the regression model of dry soil with SG smoothing was fair for clay, pH, EC, and TN at RPD = 1.90, 1.87, 1.60, and 1.43 and R2 = 0.79, 0.81, 0.64, and 0.64, respectively, while the regression model of dry soil with an SNV was good for clay, pH, EC, and TN at RPD = 2.21, 1.96, 1.70, and 1.44 and R2 = 0.84, 0.81, 0.76, 0.69, respectively. When developing predictive regression models of soil properties, the accuracy for dry soil was higher than that for wet soil, and when applying a single round of preprocessing, the regression model with SNV preprocessing was more accurate than that with SG smoothing. [ABSTRACT FROM AUTHOR]

In the pursuit of sustainable technology, the synthesis of a novel biopolymer, C‐pBPV, through the polymerization of carboxymethyl cellulose sodium salt (CMC‐Na) and viologen (BPV), with ammonium persulfate as an initiator is presented. This groundbreaking process not only promises an eco‐friendly approach but also opens avenues for advanced applications in electrochromic devices (ECDs), particularly in smart windows, displays, and beyond. Departing from conventional multi‐layered approaches, the study underscores the significance of a single‐layer material. C‐pBPV serves both as an electro‐chrome and an electrolyte, making it a compelling material for ECDs. It is used to create a gel‐based electrochromic device G1 with exceptional bistable properties. Following, a systematically controllable device, G2 is accomplished, to enhance color‐switching capabilities by adding vitamin C as an electron mediator. Both devices exhibited outstanding electrochromic properties, showcasing dual‐band absorption in the Vis‐NIR region, with color transitions from transparency to a striking purple state at low voltages. Embracing an all‐in‐one layer configuration, this study set the stage for next‐generation smart windows that not only regulate solar light for energy conservation but also pioneer a more compact, user‐friendly design, prioritizing visual comfort, sustainability, and a tailored user experience. [ABSTRACT FROM AUTHOR]

Visible and near‐infrared (vis–NIR) spectroscopy is a promising technology for the analysis of different soil quality parameters. In this study, we used in‐situ vis–NIR spectroscopy in association with partial least squares regression to predict the total and the mineral (nitrate + ammonium) nitrogen content, the permanganate oxidizable carbon (POXC), as well as the ratio of soil organic carbon‐to‐clay content in different agricultural soils in Switzerland. These parameters can indeed be used as indicators of soil quality in response to agronomic practices. To this goal, a total number of 134 soil samples were used for carbon‐, total nitrogen‐ and clay‐related parameters, whereas 69 soil samples were used for the mineral nitrogen‐related parameters. We found that the partial least squares regression model can successfully predict the total nitrogen and the POXC content as well as the ratio of soil organic carbon‐to‐clay content (ratio of performance to interquartile range, RPIQ > 2.62, R2 > 0.73, Lin's concordance correlation coefficient > 0.83). As concerns the mineral nitrogen, it was not possible to successfully predict this parameter by vis–NIR spectroscopy. By demonstrating the possibility to reliably predict POXC content and the soil organic carbon‐to‐clay ratio, we show that vis–NIR can be also used to analyse soil parameters associated with both the quality of organic carbon and the structural quality of agricultural soils. [ABSTRACT FROM AUTHOR]

In‐field soil spectroscopy represents a promising opportunity for fast soil analysis, allowing the prediction of several soil properties from one spectral reading representing one soil sample. This facilitates data acquisition from large amounts of samples through its rapidity and the absence of required chemical processing. This is of particular interest in agriculture, where the chance to retrieve information from soils directly in the field is very appealing. This review is focused on in‐field visible to near infrared (Vis–NIR) spectroscopy (350–2500 nm), aimed at analysing soils directly in the field through proximal sensing. The main scope was to explore the available knowledge to identify existing gaps limiting the reliability and robustness of in‐field measurement, to foster future research and help transition towards the practical application of this technology. For this purpose, a literature review was performed, and surveyed information encompassed sensor range, carrier platforms in use, sensor type, distance to the soil sample, measurement methodology, measured soil properties and soil management, among many others. From this, we derived a list of tools in use with their spectral measurement properties, including the potential cross‐calibration with soil spectral libraries from laboratory spectroscopy of soil samples and potential measured target soil properties. Different instruments and sensors used to measure at varying wavelength ranges and with different spectral qualities are available for a large range of prices. The most frequently analysed soil properties included soil carbon contents (soil organic carbon, soil organic matter, total carbon), texture (clay, silt, sand), total nitrogen, pH and cation exchange capacity. Future perspectives comprise the implementation of larger databases, including different instruments and cropping systems as well as methodologies combining existing knowledge regarding laboratory spectroscopy with in‐field methods. The authors highlight the need for a broadly accepted measurement protocol for in‐field soil spectroscopy, fostering harmonization and standardization and consequently a more robust application in practice. [ABSTRACT FROM AUTHOR]

A milk drink flavored with date syrup produced at a lab scale level was evaluated. The production process of date syrup involves a sequence of essential unit operations, commencing with the extraction, filtration, and concentration processes from two cultivars: Sukkary and Khlass. Date syrup was then mixed with cow's and camel's milk at four percentages to form a nutritious, natural, sweet, and energy-rich milk drink. The sensory, physical, and chemical characteristics of the milk drinks flavored with date syrup were examined. The objective of this work was to measure the physiochemical properties of date fruits and milk drinks flavored with date syrup, and then to evaluate the physical properties of milk drinks utilizing non-destructive visible–near-infrared spectra (VIS-NIR). The study assessed the characteristics of the milk drink enhanced with date syrup by employing VIS-NIR spectra and utilizing a partial least-square regression (PLSR) and artificial neural network (ANN) analysis. The VIS-NIR spectra proved to be highly effective in estimating the physiochemical attributes of the flavored milk drink. The ANN model outperformed the PLSR model in this context. RMSECV is considered a more reliable indicator of a model's future predictive performance compared to RMSEC, and the R2 value ranged between 0.946 and 0.989. Consequently, non-destructive VIS-NIR technology demonstrates significant promise for accurately predicting and contributing to the entire production process of the product's properties examined. [ABSTRACT FROM AUTHOR]

The precision agriculture is a key management tool for food security, requiring rapid and cost-effective fi eld assessment methods to support agricultural decision-making. One solution is proximal sensors that collect spectral characteristics of soils. The objective of this study was to establish models for predicting soil organic carbon (SOC) content using VIS-NIR (400–2500 nm) spectroscopy as an alternative to destructive SOC measurement methods. For the modelling, 90 soil samples were collected from 0–0.20 m depth representing the most common soil types in the Northern Great Plain (Hungary), 60 soil samples were used for calibration, and 30 soil samples for model validation. The soil samples were evaluated both chemically and physically. The estimation models were fi tted based on spectral indices, and the spectral bands used for indexing were identifi ed by principal component analysis (PCA) of refl ectance. Based on the PCA results, four SOC models were set up with moderately good coeffi cients of determination (R² =0.47–0.61). The results demonstrated that VIS-NIR spectroscopy (especially NIR) based organic carbon content estimation models are suitable for rapid estimation of soil SOC%. This can reduce sampling costs by optimizing the number of samples to be sent to the laboratory and by identifying heterogeneous patches in the study area. [ABSTRACT FROM AUTHOR]

Salix psammophila, classified under the Salicaceae family, is a deciduous, densely branched, and erect shrub. As a leading pioneer tree species in windbreak and sand stabilization, it has played a crucial role in combating desertification in northwestern China. However, different genetic sources of Salix psammophila exhibit significant variations in their effectiveness for windbreak and sand stabilization. Therefore, it is essential to establish a rapid and reliable method for identifying different Salix psammophila varieties. Visible and near-infrared (Vis-NIR) spectroscopy is currently a reliable non-destructive solution for origin traceability. This study introduced a novel feature selection strategy, called qualitative percentile weighted sampling (QPWS), based on the principle of the long tail effect for Vis-NIR spectroscopy. The core idea of QPWS combines weighted sampling and percentage wavelength selection to identify key wavelengths. By employing a multi-threaded parallel execution of multiple QPWS instances, we aimed to search for the optimal feature bands to address the instability issues that can arise during the feature selection process. To address the problem of reduced prediction performance in one-dimensional convolutional neural network (1D-CNN) models after feature selection, we have introduced convolutional autoencoders (CAEs) to reduce the dimensions of wavelengths that are discarded during feature selection. Subsequently, these reduced dimensions are fused with the selected wavelengths, thereby enhancing the model's performance. With our completed model, we selected outstanding models for model fusion and established a decision system for Salix psammophila. It is worth noting that all 1D-CNN models in this study were developed using Bayesian optimization methods. In comparison with principal component analysis (PCA) and full spectrum methods, QPWS exhibits superior predictive performance in the field of machine learning. In the realm of deep learning, the fusion of data combining QPWS with CAE demonstrated even greater potential with an improvement of average accuracy of approximately 2.13% when compared to QPWS alone and a 228% increase in operational speed compared to a model with full spectra. These results indicated that the combination of CAE with QPWS can be an effective tool for identifying the origin of Salix psammophila. [ABSTRACT FROM AUTHOR]

Soybean characterization is essential to ensure product quality during distribution according to internal values. In this context, non-destructive characterization method, such as spectroscopy, offer an effective and efficient approach to testing soybean quality in field applications. Among the instruments that are widely used for testing soybean quality, the semi-portable visible near-infrared (Vis-NIR) spectrometer operating at a specific range of 345 to 1033 nm has been proven effective. Therefore, this study aimed to investigate soybean seeds characterization using Vis-NIR spectroscopy with PCA and PLSR chemometric methods. The investigation was carried out using soybean seeds consisting of eight varieties locally produced on Java Island, Indonesia, including Dega1, Dena1, Deja2, Dering1, Devon1, Yellow Flap, Green, and Detam4, in the form of intact, crumble, flour, and paste. Several quality parameters such as protein, fat, crude fiber, carbohydrate, ash, water, chlorophyll, total carotene, vitamin C, and L*, a*, and b* values were measured across intact, crumble, flour, and paste samples. The results of Principal Component Analysis (PCA) showed that sample form and genotypes affected soybean classification. Furthermore, Partial Least Squares Regression (PLSR) showed adequate model calibration for crude fiber, chlorophyll, total carotene, and vitamin C parameters. Based on this analysis, it could be concluded that Vis-NIR spectroscopy proved to be suitable for the classification and prediction of soybean characterization.

In soil science, the allocation of soil samples to their respective origins holds paramount significance, as it serves as a crucial investigative tool. In recent times, with the increasing use of proximal sensing and advancements in machine-learning techniques, new approaches have accompanied these developments, enhancing the effectiveness of soil utilization in soil science. This study investigates soil classification based on four parent materials. For this purpose, a total of 59 soil samples were collected from 12 profiles and the vicinity of each profile at a depth of 0–30 cm. Surface soil samples were analyzed for elemental concentrations using X-Ray fluorescence (XRF) and inductively coupled plasma–optical emission spectrometry (ICP-OES) and soil spectra using a visible near-infrared (Vis-NIR) spectrometer. Soil samples collected from soil profiles (12 soil samples) and surface (47 soil samples) were used to classify parent materials using machine learning-based algorithms such as Support Vector Machine (SVM), Ensemble Subspace k-Near Neighbor (ESKNN), and Ensemble Bagged Trees (EBTs). Additionally, as a validation of the classification techniques, the dataset was subjected to five-fold cross-validation and independent sample set splitting (80% calibration and 20% validation). Evaluation metrics such as accuracy, F score, and G mean were used to evaluate prediction performance. Depending on the dataset and algorithm used, the classification success rates varied between 70% and 100%. Overall, the ESKNN (99%) produced better results than other classification methods. Additionally, Relief algorithms were employed to identify key variables for each dataset (ICP-OES: CaO, Fe2O3, Al2O3, MgO, and MnO; XRF: SiO2, CaO, Fe2O3, Al2O, and MnO; Vis-NIR: 567, 571, 572, 573, and 574 nm). Subsequent soil reclassification using these reduced variables revealed reduced accuracies using Vis-NIR data, with ESKNN still yielding the best results.

ABSTRACTInfrared heating has been widely used in food processing; however, there are few studies on the VIS-NIR radiation properties of food. We measured the radiation properties of starchy foods (potato, Chinese yam and sweet potato) and protein foods (beef tenderloin, pork tenderloin and chicken breast) and dispersions of starches and proteins before and after heat treatment in the 0.38–2.5 μm band. The results show that the radiation penetration depths (RPDs) of all tested foods are relatively high in the 0.6–1.3 μm band, with maximum values in the range of 4–8 mm. After heat treatment, the RPDs of starchy foods slightly increase, while those of protein foods decrease to approximately 1/2 of the original values. Comparative heating experiments of potato, Chinese yam and sweet potato heated by a halogen lamp (1.04 μm) and a carbon fiber lamp (1.76 μm) with peak wavelengths in and out of the 0.6–1.3 μm band, respectively, show that the foods heated by the halogen lamp have faster heating rates and more uniform temperature distributions. Because starchy and protein foods have high RPDs in the 0.6–1.3 μm band, the VIS-NIR radiation containing this band is expected to achieve rapid heating.

In the pursuit of enhancing the wine production process through the utilization of new technologies in viticulture, this study presents a novel approach for the rapid assessment of wine grape maturity levels using non-destructive, in situ infrared spectroscopy and artificial intelligence techniques. Building upon our previous work focused on estimating sugar content ( ∘ Brix) from the visible and near-infrared (VNIR) and short-wave infrared (SWIR) regions, this research expands its scope to encompass pH and titratable acidity, critical parameters determining the grape maturity degree, and in turn, wine quality, offering a more representative estimation pathway. Data were collected from four grape varieties—Chardonnay, Malagouzia, Sauvignon Blanc, and Syrah—during the 2023 harvest and pre-harvest phenological stages in the vineyards of Ktima Gerovassiliou, northern Greece. A comprehensive spectral library was developed, covering the VNIR–SWIR spectrum (350–2500 nm), with measurements performed in situ. Ground truth data for pH, titratable acidity, and sugar content were obtained using conventional laboratory methods: total soluble solids (TSS) ( ∘ Brix) by refractometry, titratable acidity by titration (expressed as mg tartaric acid per liter of must) and pH by a pH meter, analyzed at different maturation stages in the must samples. The maturity indicators were predicted from the point hyperspectral data by employing machine learning algorithms, including Partial Least Squares regression (PLS), Random Forest regression (RF), Support Vector Regression (SVR), and Convolutional Neural Networks (CNN), in conjunction with various pre-processing techniques. Multi-output models were also considered to simultaneously predict all three indicators to exploit their intercorrelations. A novel multi-input–multi-output CNN model was also proposed, incorporating a multi-head attention mechanism and enabling the identification of the spectral regions it focuses on, and thus having a higher interpretability degree. Our results indicate high accuracy in the estimation of sugar content, pH, and titratable acidity, with the best models yielding mean R 2 values of 0.84, 0.76, and 0.79, respectively, across all properties. The multi-output models did not improve the prediction results compared to the best single-output models, and the proposed CNN model was on par with the next best model. The interpretability analysis highlighted that the CNN model focused on spectral regions associated with the presence of sugars (i.e., glucose and fructose) and of the carboxylic acid group. This study underscores the potential of portable spectrometry for real-time, non-destructive assessments of wine grape maturity, thereby providing valuable tools for informed decision making in the wine production industry. By integrating pH and titratable acidity into the analysis, our approach offers a holistic view of grape quality, facilitating more comprehensive and efficient viticultural practices. [ABSTRACT FROM AUTHOR]

Fishing has provided mankind with a protein-rich source of food and labor, allowing for the development of an important industry, which has led to the overexploitation of most targeted fish species. The sustainable management of these natural resources requires effective control of fish landings and, therefore, an accurate calculation of fishing quotas. This work proposes a deep learning-based spatial-spectral method to classify five pelagic species of interest for the Chilean fishing industry, including the targeted Engraulis ringens, Merluccius gayi, and Strangomera bentincki and non-targeted Normanichthtys crockeri and Stromateus stellatus fish species. This proof-of-concept method is composed of two channels of a convolutional neural network (CNN) architecture that processes the Red–Green–Blue (RGB) images and the visible and near-infrared (VIS-NIR) reflectance spectra of each species. The classification results of the CNN model achieved over 94% in all performance metrics, outperforming other state-of-the-art techniques. These results support the potential use of the proposed method to automatically monitor fish landings and, therefore, ensure compliance with the established fishing quotas. [ABSTRACT FROM AUTHOR]

This study aimed to establish sound relationships between soil properties of agricultural land in central Spain and their spectral attributes to contribute to deriving an indicator for sustainable farm management. Sixteen plots, managed under various conditions, eight with traditional tillage and eight with other alternative managements, were selected to gather soil samples representing three predominant soil orders (Leptosols, Cambisols, and Luvisols). Soil sampling was conducted from depths ranging from 0 to 30 cm (0–50; 5–10, 10–20, and 20–30 cm), ensuring a broad spectrum of sample variability across different times and locations. The reflectance of soil 64 soil samples was measured within the range of 400 to 900 nm, and the corresponding concentrations of soil organic carbon and majoritarian minerals, calcium carbonate, quartz, phyllosilicates, K-Feldspar, and plagioclase were determined for each sample. Partial least squares analysis was employed to construct prediction models using a calibration dataset comprising 66% of randomly selected samples. The remaining 33% of samples were utilized for model validation. The prediction models for the measured soil chemical properties yielded R2 values ranging from 0.14 to 0.79. Only SOC and limestone provided accurate prediction models. These findings hold promise for developing a soil health indicator tailored for site-specific crop management. However, the complex composition of soil organic carbon and calcium carbonate in certain soils underscores the importance of careful interpretation and validation of remote sensing data, as well as the need for advanced modeling approaches that can account for the interactions between multiple soil constituents.

In this study, a regression model of paddy soil properties using diffuse reflectance spectroscopy was developed to replace chemical soil analysis as a more efficient alternative. Soil samples were collected and analyzed from saltwater paddy fields located in Jeongnam-myeon, Hwaseong-si, Gyeonggi-do in the Republic of Korea, and the spectral data of wet and dry soil were collected. The regression models were compared and analyzed using partial least squares regression (PLSR) with Savitzky–Golay smoothing (SG smoothing) and Standard Normal Variate (SNV) preprocessing to predict the soil properties. Analysis showed that the predictive regression model of wet soil with SG smoothing and an SNV did not meet the evaluation criteria of a fair model. However, the regression model of dry soil with SG smoothing was fair for clay, pH, EC, and TN at RPD = 1.90, 1.87, 1.60, and 1.43 and R2 = 0.79, 0.81, 0.64, and 0.64, respectively, while the regression model of dry soil with an SNV was good for clay, pH, EC, and TN at RPD = 2.21, 1.96, 1.70, and 1.44 and R2 = 0.84, 0.81, 0.76, 0.69, respectively. When developing predictive regression models of soil properties, the accuracy for dry soil was higher than that for wet soil, and when applying a single round of preprocessing, the regression model with SNV preprocessing was more accurate than that with SG smoothing.

In the present work CuS coated cotton thread was synthesized by co-precipitation method with (WS) and without stirring effect (WOS). XRD and FESEM were used to analyze the structural morphology of CuS over thread. The crystallite size for WS and WOS were 25.1 nm and 22.1 nm. The band calculated were 1.48 and 1.60 eV for WS and WOS. The XPS analysis confirm the presence of Cu+, Cu2+, Cu° charge state. MB organic dye was tested for degradation and reported degradation up to 94% of 50 ml MB dye in 20 min with using low catalytic weight 0.4 gm. IV measurement was also taken to study the electronic properties of coated threads. The high current under light presence and high degradation of MB dye through WOS thread is discussed in this work.

Traditional methods for obtaining soil heavy metal content are expensive, inefficient, and limited in monitoring range. In order to meet the needs of soil environmental quality evaluation and health status assessment, visible near-infrared spectroscopy and XRF spectroscopy for monitoring heavy metal content in soil have attracted much attention, because of their rapid, nondestructive, economical, and environmentally friendly features. The use of either of these spectra alone cannot meet the accuracy requirements of traditional measurements, while the synergistic use of the two spectra can further improve the accuracy of monitoring heavy metal lead content in soil. Therefore, this study applied various spectral transformations and preprocessing to vis-NIR and XRF spectra; used the whale optimization algorithm (WOA) and competitive adaptive re-weighted sampling (CARS) algorithms to identify feature spectra; designed a combination variable model (CVM) based on multi-layer spectral data fusion, which improved the spectral preprocessing and spectral feature screening process to increase the efficiency of spectral fusion; and established a quantitative model for soil Pb concentration using partial least squares regression (PLSR). The estimation performance of three spectral fusion strategies, CVM, outer-product analysis (OPA), and Granger-Ramanathan averaging (GRA), was discussed. The results showed that the accuracy and efficiency of the CARS algorithm in the fused spectra estimation model were superior to those of the WOA algorithm, with an average coefficient of determination (R2) value of 0.9226 and an average root mean square error (RMSE) of 0.1984. The accuracy of the estimation models established, based on the different spectral types, to predict the Pb content of the soil was ranked as follows: the CVM model > the XRF spectral model > the vis-NIR spectral model. Within the CVM fusion strategy, the estimation model based on CARS and PLSR (CARS_D1+D2) performed the best, with R2 and RMSE values of 0.9546 and 0.2035, respectively. Among the three spectral fusion strategies, CVM had the highest accuracy, OPA had the smallest errors, and GRA showed a more balanced performance. This study provides technical means for on-site rapid estimation of Pb content based on multi-source spectral fusion and lays the foundation for subsequent research on dynamic, real-time, and large-scale quantitative monitoring of soil heavy metal pollution using high-spectral remote sensing images. [ABSTRACT FROM AUTHOR]

The sample representativeness, spectral acquisition conditions, and modeling process would affect the practical application value and application range of a model. To establish an applicative online evaluation model for soluble solids content (SSC) of Leibo navel orange, transmittance spectra with the wavelength of 650–950 nm were collected under different sample placement postures and transmission speeds using an online system with four parallel spectrometers. These samples were gathered from three production points at three harvest times and contained four grades with single fruit weight ranging from 133.8 to 645.0 g. Considering the heterogeneity of navel orange and the practical application of online detection, all spectra of the same sample were used to join in modeling, and a series of models for SSC were established. The models by average spectra of four parallel spectrometers got better results than anyone. The accuracy of the model was the best when samples were placed horizontally on the sample tray, followed by stem upward, and stem downward worst. The regression model for SSC by using spectra with horizontal orientation and speed of 0.5 m/s got the best performance in terms of root mean square error of prediction (RMSEP) and residual predictive deviation (RPD), with values of 0.442% and 2.77, respectively. The overall results showed that the research method and system can be applied to the online detection of navel orange quality. [ABSTRACT FROM AUTHOR]

Tomatoes (Solanum lycopersicum L.) are a widely used vegetable in the human diet throughout the year, both fresh and in various processed products. Tomatoes contain compounds important to human health and are an important source of vitamins, antioxidants, and mineral elements. Performing biochemical analyses is an expensive, environmentally unfriendly and time-consuming process; therefore, a way to determine the biochemical composition of tomatoes using non-destructive methods is being sought. The study includes 45 varieties of tomatoes with different colors - red, pink, orange, brown, yellow, and bicolor tomato fruits. The content of dry matter, soluble dry matter, titratable acidity, lycopene, ß-carotene, total phenol, and flavonoids was determined by standard biochemical procedure. Reflectance spectrums of tomato fruits were obtained with Remote Sensing Portable Spectroradiometer RS-3500 (Ltd. Spectral Evolution, Haverhill, MA, USA) at the wavelength 350-2,500 nm with a 1 nm interval. In order to determine the content of various biochemical parameters in tomatoes, the vegetation indices found in the literature were used, and new ones were developed. The research demonstrated that the developed vegetative indices allow to detect lycopene and ß-carotene content non-destructively. For the determination of the dry matter, soluble solids and phenolic content, indices designed for detecting water content can be used, but their correlation coefficients with chemical methods are moderately high - 0.65, 0.56 and 0.57, respectively. It was found that the best correlation between biochemically detected parameters and vegetation indices is for lycopene > ß-carotene > dry matter> total phenols = titratable acidity = soluble solids > taste index > flavonoids. [ABSTRACT FROM AUTHOR]

Introduction: Fresh pomegranate fruit is susceptible to bruising, a common type of mechanical damage during harvest and at all stages of postharvest handling. Accurate and early detection of such damages in pomegranate fruit plays an important role in fruit grading. This study investigated the detection of bruises in fresh pomegranate fruit using hyperspectral imaging technique. Methods: A total of 90 sample of pomegranate fruit were divided into three groups of 30 samples, each representing purposefully induced pre-scanning bruise by dropping samples from 100 cm and 60 cm height on a metal surface. The control has no pre-scanning bruise (no drop). Two hyperspectral imaging setups were examined: visible and near infrared (400 to 1000 nm) and short wavelength infrared (1000 to 2500 nm). Region of interest (ROI) averaged reflectance spectra was implemented to reduce the image data. For all hypercubes a principal components analysis (PCA) based background removal were done prior to segmenting the region of interest (ROI) using the Evince® multi-variate analysis software 2.4.0. Then the average spectrum of the ROI of each sample was computed and transferred to the MATLAB 2022a (The MathWorks, Inc., Mass., USA) for classification. A two-layer feed-forward artificial neural network (ANN) is used for classification. Results and discussion: The accuracy of bruise severity classification ranged from 80 to 96.7%. When samples from both bruise severity (Bruise damage induced from a 100cm and 60 cm drop heights respectively) cases were merged, class recognition accuracy were 88.9% and 74.4% for the SWIR and Vis-NIR, respectively. This study implemented the method of selecting out informative bands and disregarding the redundant ones to decreases the data size and dimension. The study developed a more compact classification model by the data dimensionality reduction method. This study demonstrated the potential of using hyperspectral imaging technology in sensing and classification of bruise severity in pomegranate fruit. This work provides the foundation to build a compact and fast multispectral imaging-based device for practical farm and packhouse applications. [ABSTRACT FROM AUTHOR]

Various epitaxial mechanisms have been proposed to control the growth orientation of vapor‐deposited nanowires, yet the required lattice matching between target nanowires and supporting substrates limits their applicability. In this work, a versatile hot stamping protocol for fabricating parallel hydrophobic nanogrooves on flexible polymer films (e.g., polyimide (PI), polyethylene naphthalate (PEN), polydimethylsiloxane (PDMS)) is proposed. More interestingly, various organic small molecules, including several metal phthalocyanines (MPc, M = Cu, Zn, Fe, Ni, Co), 9,10‐bis(phenylethynyl)anthracene (BPEA), 9,10‐diphenylanthracene (DPA), and tris‐(8‐hydroxyquinoline)aluminium (Alq3), are directly assembled into horizontally‐oriented nanowires along the hot‐stamped nanogrooves on a flexible PI film, thereby breaking the lattice‐matching limitation for oriented nanowire growth. These submillimeter‐long horizontally oriented nanowires can be integrated into flexible photodetectors directly on their growth film, eliminating the need for laborious post‐growth transfer and alignment steps and the associated structural damage and contamination. Consequently, the in situ integrated flexible photodetector made of aligned CuPc nanowires maintains a stable and fast photoresponse to a spectrum in the region of 405‐980 nm even when the detector is bent to a radius of curvature of 2.5 mm and 1000 times. This work will open new opportunities to develop in situ integrated flexible devices based on organic crystalline nanowires for practical applications. [ABSTRACT FROM AUTHOR]

Butterfly-pea (Clitoria ternatea L.) extract powder is a functional product with numerous benefits obtained by extraction followed by the drying process. During drying, encapsulations can be added to protect the color and antioxidants of the samples. Using visible-near-infrared (Vis–NIR) spectroscopy, this research aimed to detect maltodextrin and soybean protein isolate (SPI) added as encapsulants to butterfly-pea extract powder. Butterfly-pea extract powder were added with 10, 20, 30, 40, and 50% concentrations of maltodextrin and SPI. Spectral data were acquired using a Vis–NIR fiber optic spectrometer at 350–1,000 nm. The chemometric methods used were principal component analysis (PCA), PCA-discriminant analysis (PCA–DA), partial least square regression (PLSR), and partial least square discriminant analysis (PLS-DA). The results showed that PCA can discriminate pure and maltodextrin- and SPI-added samples using low principal components. PCA-DA determined the accuracy levels of 88% for maltodextrin and 94.67% for SPIs. The PLSR models predicted the addition of maltodextrin with the following variables: coefficient of determination of calibration (R c 2), 0.98; coefficient of determination of prediction (R p 2), 0.98; root mean square error of calibration (RMSEC), 2.1%; and root mean square error of prediction (RMSEP), 4.02%. The values for the addition of SPI were R c 2 of 0.97, R p 2 of 0.97, RMSEC of 2.72%, and RMSEP of 2.83%. The PLS-DA models resulted in an accuracy of 98 and 91% for the identification of maltodextrin and SPI, respectively. In conclusion, this research showed the potency of Vis–NIR spectroscopy combined with a proper chemometric analysis to detect additives in butterfly-pea extract powders.

IntroductionFresh pomegranate fruit is susceptible to bruising, a common type of mechanical damage during harvest and at all stages of postharvest handling. Accurate and early detection of such damages in pomegranate fruit plays an important role in fruit grading. This study investigated the detection of bruises in fresh pomegranate fruit using hyperspectral imaging technique.MethodsA total of 90 sample of pomegranate fruit were divided into three groups of 30 samples, each representing purposefully induced pre-scanning bruise by dropping samples from 100 cm and 60 cm height on a metal surface. The control has no pre-scanning bruise (no drop). Two hyperspectral imaging setups were examined: visible and near infrared (400 to 1000 nm) and short wavelength infrared (1000 to 2500 nm). Region of interest (ROI) averaged reflectance spectra was implemented to reduce the image data. For all hypercubes a principal components analysis (PCA) based background removal were done prior to segmenting the region of interest (ROI) using the Evince® multi-variate analysis software 2.4.0. Then the average spectrum of the ROI of each sample was computed and transferred to the MATLAB 2022a (The MathWorks, Inc., Mass., USA) for classification. A two-layer feed-forward artificial neural network (ANN) is used for classification.Results and discussionThe accuracy of bruise severity classification ranged from 80 to 96.7%. When samples from both bruise severity (Bruise damage induced from a 100cm and 60 cm drop heights respectively) cases were merged, class recognition accuracy were 88.9% and 74.4% for the SWIR and Vis-NIR, respectively. This study implemented the method of selecting out informative bands and disregarding the redundant ones to decreases the data size and dimension. The study developed a more compact classification model by the data dimensionality reduction method. This study demonstrated the potential of using hyperspectral imaging technology in sensing and classification of bruise severity in pomegranate fruit. This work provides the foundation to build a compact and fast multispectral imaging-based device for practical farm and packhouse applications.

In-situ infrared soil spectroscopy is prone to the effects of ambient factors, such as moisture, shadows, or roughness, resulting in measurements of compromised quality, which is amplified when multiple sensors are used for data collection. Aiming to provide accurate estimations of common physicochemical soil properties, such as soil organic carbon (SOC), texture, pH, and calcium carbonates based on in-situ reflectance captured by a set of low-cost spectrometers operating at the shortwave infrared region, we developed an AI-based spectral transfer function that maps fields to laboratory spectra. Three test sites in Cyprus, Lithuania, and Greece were used to evaluate the proposed methodology, while the dataset was harmonized and augmented by GEO-Cradle regional soil spectral library (SSL). The developed dataset was used to calibrate and validate machine learning models, with the attained predictive performance shown to be promising for directly estimating soil properties in-situ, even with sensors with reduced spectral range. Aiming to set a baseline scenario, we completed the exact same modeling experiment under laboratory conditions and performed a one-to-one comparison between field and laboratory modelling accuracy metrics. SOC and pH presented an R2 of 0.43 and 0.32 when modeling the in-situ data compared to 0.63 and 0.41 of the laboratory case, respectively, while clay demonstrated the highest accuracy with an R2 value of 0.87 in-situ and 0.90 in the laboratory. Calcium carbonates were also attempted to be modeled at the studied spectral region, with the expected accuracy loss from the laboratory to the in-situ to be observable (R2 = 0.89 for the laboratory and 0.67 for the in-situ) but the reduced dataset variability combined with the calcium carbonate characteristics that are spectrally active in the region outside the spectral range of the used in-situ sensor, induced low RPIQ values (less than 0.50), signifying the importance of the suitable sensor selection. [ABSTRACT FROM AUTHOR]

Understanding and determining soil properties is reflected in improving farm management and crop production. Soil salinity, pH and calcium carbonate are among the factors affecting the soil's physical and chemical properties. Hence, their estimation is very important for agricultural management, especially in arid regions (Wadi Elkobaneyya valley, located in the northwest of Aswan Governorate, Upper Egypt). The study objectives were to characterize and develop prediction models for soil salinity, pH and calcium carbonate (CaCO3) using integration soil analysis and spectral reflectance vis-NIR spectroscopy. To achieve the study objectives, three multivariate regression models: Partial Least Squares Regression (PLSR), Multivariate Adaptive Regression Splines (MARS) and Least Square-Support Vector Regression (LS-SVR)); and two machine learning algorithms, Random Forest (RF) and Artificial Neural Networks (ANN) were used. Ninety-six surface soil samples were collected from the study area at depths 0–5 cm. The data were divided into a calibration dataset (70% of the total) and a validation dataset (30% of the total dataset). The obtained results represent that the PLSR model was the best model for soil pH parameters where R2 of calibration and validation predictability = 0.68 and 0.52, respectively. The LS-SVR model was the best model to predict soil Electrical Conductivity (EC) and soil Calcium Carbonate (CaCO3) content, with R2 0.70 and 0.74 for calibration and R2 0.26 and 0.47 for validation, respectively. On the other hand, the results of the implemented machine learning algorithm model showed that RF was the best model to predict soil pH and CaCO3, as the R2 was 0.82 for calibration and 0.57 for validation, respectively. Nevertheless, the best model for predicting soil EC was ANN, with an R2 of 0.96 for calibration and 64 for validation. The results show the advantages of machine learning models for predicting soil EC, pH and CaCO3 by Vis-NIR spectroscopy. Therefore, Vis-NIR spectroscopy is considered faster and more cost-efficient and can be further used in environmental monitoring and precision farming. [ABSTRACT FROM AUTHOR]

As the need for sensor fusion systems has grown, developing methods to find correspondences between images with different spectral ranges has become increasingly important. Since most images do not share low-level information, such as textures and edges, existing matching approaches fail even with convolutional neural networks (CNNs). In this paper, we propose an end-to-end metric learning method, called SPIMNet (SPectral-Invariant Matching Network) for robust cross- and multi-spectral image patch matching. While existing methods based on CNNs learn matching features directly from cross- and multi-spectral image patches, SPIMNet transforms across spectral bands and discriminates for similarity in three steps. First, (1) SPIMNet is adjusted for a feature domain by introducing a domain translation network; then (2) two Siamese networks learn to match the adjusted features with the same spectral domain; and (3) the matching features are fed to fully-connected layers to determine the identity of the patches as a classification task. By effectively incorporating each step, SPIMNet achieved competitive results on a variety of challenging datasets, including both VIS–NIR and VIS–Thermal image pairs. Our code is available at https://github.com/koyeongmin/SPIMNet. [Display omitted] • The method is based on domain conversion for matching between VIS and NIR images. • The method uses metric learning to integrate information from the two domains. • This work studies the effect of the domain conversion method and loss function. • This work achieves noteworthy performance in three public datasets. • The result can be adapted to various applications such as depth estimation. [ABSTRACT FROM AUTHOR]

Organic and non-organic soybean flours, although visually indifferent, have a significant difference in price and nutrition content. Therefore, the accurate authentication detection of organic soybean flour is necessary. Visible-near-infrared (Vis-NIR) spectroscopy coupled with chemometric methods is a non-destructive technique applied to detect authentic or adulterated organic soybean flour. The spectra of organic, adulterated organic, and nonorganic soybean flours were captured using a Vis-NIR spectrometer at 350-1000 nm. The spectra were analyzed using partial least squares (PLS), principal component analysis (PCA), and the combination of these two with discriminant analysis (DA). The results showed that PCA using PC1 and PC2 could differentiate organic and non-organic soybean flours, whereas PC1 and PC4 can detect pure and adulterated organic soybean flours. The PCA-linear DA models showed 98.5% accuracy (Acc) for predicting pure organic and adulterated soybean flours and 100% Acc for predicting organic and non-organic flours. Moreover, PLS regression models resulted in a high R² of >95% for predicting organic and non-organic flours and pure and adulterated soybean flours. In addition, the PLS-DA models can differentiate organic from non-organic soybean flour and distinguish pure and adulterated soybean flours with 100% Acc and reliability. [ABSTRACT FROM AUTHOR]

Spectroscopy is a widely used technique that can contribute to food quality assessment in a simple and inexpensive way. Especially in grape production, the visible and near infrared (VNIR) and the short-wave infrared (SWIR) regions are of great interest, and they may be utilized for both fruit monitoring and quality control at all stages of maturity. The aim of this work was the quantitative estimation of the wine grape ripeness, for four different grape varieties, by using a highly accurate contact probe spectrometer that covers the entire VNIR–SWIR spectrum (350–2500 nm). The four varieties under examination were Chardonnay, Malagouzia, Sauvignon-Blanc, and Syrah and all the samples were collected over the 2020 and 2021 harvest and pre-harvest phenological stages (corresponding to stages 81 through 89 of the BBCH scale) from the vineyard of Ktima Gerovassiliou located in Northern Greece. All measurements were performed in situ and a refractometer was used to measure the total soluble solids content (°Brix) of the grapes, providing the ground truth data. After the development of the grape spectra library, four different machine learning algorithms, namely Partial Least Squares regression (PLS), Random Forest regression, Support Vector Regression (SVR), and Convolutional Neural Networks (CNN), coupled with several pre-treatment methods were applied for the prediction of the °Brix content from the VNIR–SWIR hyperspectral data. The performance of the different models was evaluated using a cross-validation strategy with three metrics, namely the coefficient of the determination ( R 2 ), the root mean square error (RMSE), and the ratio of performance to interquartile distance (RPIQ). High accuracy was achieved for Malagouzia, Sauvignon-Blanc, and Syrah from the best models developed using the CNN learning algorithm ( R 2 > 0.8 , RPIQ ≥ 4 ), while a good fit was attained for the Chardonnay variety from SVR ( R 2 = 0.63 , RMSE = 2.10 , RPIQ = 2.24 ), proving that by using a portable spectrometer the in situ estimation of the wine grape maturity could be provided. The proposed methodology could be a valuable tool for wine producers making real-time decisions on harvest time and with a non-destructive way. [ABSTRACT FROM AUTHOR]

The optical and electrical properties of sodium niobate crystals and ceramics doped with Mn atoms are studied. Na(Nb1−zMnz)O3 crystals exhibit actual z = 0–1.0 wt% Mn content. The Na(Nb1−zMnz)O3 ceramics contains Mn from the 0–5 wt% range. The (Na1−xBix)(Nb1−yMny)O3 ceramics is codoped with x = 0.015–0.50 and y = 0.01–0.33. Mn addition decreases the reflectance magnitude. Combined nonstoichiometry and local disorder in the crystal lattice, resulting from Mn doping and oxygen vacancies, induce contributions to the electronic structure. The visible and near‐infrared (Vis–NIR) diffuse reflectance, R(λ), spectra include a linear combination of contributions. The modified Kubelka–Munk function representation allows estimation of the optical energy gap, Egap. This estimation is qualitative because of the complex structure of the samples. Two optical gaps, showing dependence on Mn content, are discerned when Mn content <1 wt%. Egap,high changes toward the Shockley–Queisser limit, from 1.85 to 1.4 eV, when Mn content is low (z < 1 wt%). Simultaneous occurrence of Egap,low of 1.0–1.3 eV suggests tuning possibility. Egap of ≈1 eV dominates optical properties when Mn content is >1 wt%. The optical features are consistent with activation energy attributed to thermally generated electrical conductivity. [ABSTRACT FROM AUTHOR]

Infrared heating has been widely used in food processing; however, there are few studies on the VIS-NIR radiation properties of food. We measured the radiation properties of starchy foods (potato, Chinese yam and sweet potato) and protein foods (beef tenderloin, pork tenderloin and chicken breast) and dispersions of starches and proteins before and after heat treatment in the 0.38–2.5 μm band. The results show that the radiation penetration depths (RPDs) of all tested foods are relatively high in the 0.6–1.3 μm band, with maximum values in the range of 4–8 mm. After heat treatment, the RPDs of starchy foods slightly increase, while those of protein foods decrease to approximately 1/2 of the original values. Comparative heating experiments of potato, Chinese yam and sweet potato heated by a halogen lamp (1.04 μm) and a carbon fiber lamp (1.76 μm) with peak wavelengths in and out of the 0.6–1.3 μm band, respectively, show that the foods heated by the halogen lamp have faster heating rates and more uniform temperature distributions. Because starchy and protein foods have high RPDs in the 0.6–1.3 μm band, the VIS-NIR radiation containing this band is expected to achieve rapid heating. [ABSTRACT FROM AUTHOR]

Chemicals are often added to turmeric to increase profits, posing significant health risks to consumers. At the same time, traditional methods for detecting contaminants in turmeric are complicated and time-consuming. This study aimed to develop a more practical approach using visible-near infrared (Vis-NIR) and multispectral imaging (MSI) techniques to detect starch adulteration in turmeric. The turmeric powder was mixed with starch (0.1, 0.5, 1, 2.5, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, and 100 % (w/w)) to create spectral and MSI datasets within the 400–1050 nm wavelength range. Spectra were corrected using spectral preprocessing techniques such as Savitzky-Golay (SG), Multiplicative Scatter Correction (MSC), and Standard Normal Variate (SNV). Principal Component Analysis (PCA) applied to reduce the dimensions and various machine learning (ML) models for prediction. The Random Forest Regressor (RFR) achieved a coefficient of determination (R²) of 0.999, a root mean squared error (RMSE) of 0.391 mg (w/w), and a residual predictive deviation (RPD) of 92.3 % in regression analysis. For classification, the Random Forest Classifiers (RFC) achieved an F1 score of 96.0 % and a Matthews Correlation Coefficient (MCC) of 94.6 %. In MSI analysis, the DenseNet201 model obtained an F1 score of 92.9 % and an MCC of 91.9 %. Moreover, the robustness of these models was cross-validated using leave-one-out cross-validation (LOOCV) and K-fold methods. The significance of the study lies in several critical areas, such as public health, advancement in technology, etc. The study's findings reveal that Vis-NIR and MSI approaches are excellent in detecting starch adulteration in turmeric with reliability. It has important implications for public health and food safety by offering a reliable tool for verifying the purity of turmeric and other food items. • Spectroscopic and MSI system developed to identify starch in turmeric. • Three spectral pre-processing: SG, MSC, and SNV were used with PCA. • PCA Loading plot confirms the starch wavelengths. • RFR achieved R2 of 0.999 and RMSE of 0.391 mg (w/w) with the spectroscopy. • DenseNet201 achieved an F1 score of 92.9 % with MSI [ABSTRACT FROM AUTHOR]

On-time and accurate estimation of the soil nitrogen mineralization rate (SNMR) is critical for nitrogen (N) management and protecting the environment. This study evaluated the performance of a visible-to-near-infrared reflectance (vis-NIR) spectroscopy for predicting SNMR for four texture groups. A total of 62 topsoil samples were collected from 17 management zones distributed over four fields and incubated with seven destructive sampling events. Samples were analysed for total mineral N (NH 4 ++NO 3 –) content and scanned using a vis-NIR sensor simultaneously at each of the seven-sampling times. Four partial least squares regression models were calibrated and validated for four textural groups (groups- 1– 4) identified over the United State Department of Agriculture (USDA) texture triangle. Prediction accuracies indicated that vis-NIR sensor was moderately to highly accurate for predicting SNMR, while observing variable accuracies across texture groups. The highest accuracy was obtained for group 1 (sandy-loam; coefficient of determination, R2 = 0.90; root mean square error, RMSE = 0.04 mg N kg−1 soil day−1), successively followed by group 2 (mostly loam; R2 = 0.80, RMSE = 0.05 mg N kg−1 soil day−1) group 4 (mostly silt; R2 = 0.66, RMSE = 0.08 mg N kg−1 soil day−1), and group 3 (silt-loam; R2 = 0.44, RMSE = 0.08 mg N kg−1 soil day−1). Variable importance in projection score revealed that the key spectral bands to predict SNMR were in 2150 – 2260 nm and 2470 – 2480 nm, resembling the key bands associated with soil organic compounds and clay minerals. In-advance texture information required for soil stratification is regarded a limitation of the proposed approach. In conclusion, vis-NIR holds potential for a rapid estimation of SNMR when samples are stratified into similar texture groups in advance, however, confirmatory research will be needed to validate the current findings for soils from different origin and under different management. • Texture based sample stratification enables vis-NIRS to predict soil N mineralization rate. • Vis-NIR sensor can predict soil N mineralization rate at moderate to high accuracy. • The highest prediction accuracy is possible for sandy-loam texture. • Spectral bands of 2150–2260 & 2470–2480 nm are significant for SNMR prediction. [ABSTRACT FROM AUTHOR]

[Display omitted] • S-type heterojunctions of MIL-53(Fe)/Ag 2 O on charcoal sponge (CS) was realized. • The combination of photocatalysis and Fenton effectively improve the removal efficiency of compound pollutants. • Inhibition zone tests demonstrated that the toxicity of NFX intermediates to E. coli gradually decreased as the degradation of NFX progressed. • CS/MIL-53(Fe)/Ag 2 O exhibits improved separation efficiency of photoinduced carriers and REDOX capacity. • DFT calculations were used to probe the electronic structures and reaction mechanism. This study addresses the urgent need to develop easily recoverable and efficient photocatalysts for the treatment of compound polluted wastewater. Herein, S-scheme MIL-53(Fe)/Ag 2 O heterojunctions on a charcoal sponge (CS) were constructed by solvothermal-chemical precipitation method, exhibiting high separation efficiency of photoinduced carriers and reduction–oxidation (REDOX) capacity. The synthesized CS/MIL-53(Fe)/Ag 2 O was characterized by SEM, TEM, XRD, and XPS, confirming the successful deposition and uniform distribution of MIL-53(Fe) and Ag 2 O nanoparticles on CS. Moreover, CS/MIL-53(Fe)/Ag 2 O exhibits an extensive light absorption range from visible to near-infrared (NIR) light, thereby significantly enhancing its photocatalytic activity. In present of H 2 O 2 , CS/MIL-53(Fe)/Ag 2 O demonstrated outstanding removal efficacy of norfloxacin (NFX, 96 %, pH=7) or Cr(VI) (100 %, pH=3) through the photocatalytic-Fenton reaction, which was notably superior to that of CS/MIL-53(Fe) (50.4 % NFX, 56.8 % Cr(VI)) and CS/Ag 2 O (44.3 % NFX, 38 % Cr(VI)). Importantly, when CS/MIL-53(Fe)/Ag 2 O was used to treat combined polluted wastewater containing both Cr(VI) and NFX, the removal efficiency of Cr(VI) and NFX showed a significant synergistic effect. Additionally, inhibition zone tests demonstrated that the toxicity of NFX intermediates to E. coli gradually decreased as the degradation of NFX progressed. Furthermore, density functional theory (DFT) calculation and relevant characterization were conducted to elucidate the reaction mechanism of S-scheme heterojunctions, highlighting the enhanced separation rate of photo-induced carriers and REDOX capability. This work not only offers a promising approach for construction of MOF-based S-scheme semiconductor heterojunctions, but also presents promising prospects for the purification of compound polluted wastewater containing norfloxacin and Cr(VI). [ABSTRACT FROM AUTHOR]

Fishing has provided mankind with a protein-rich source of food and labor, allowing for the development of an important industry, which has led to the overexploitation of most targeted fish species. The sustainable management of these natural resources requires effective control of fish landings and, therefore, an accurate calculation of fishing quotas. This work proposes a deep learning-based spatial-spectral method to classify five pelagic species of interest for the Chilean fishing industry, including the targeted Engraulis ringens, Merluccius gayi, and Strangomera bentincki and non-targeted Normanichthtys crockeri and Stromateus stellatus fish species. This proof-of-concept method is composed of two channels of a convolutional neural network (CNN) architecture that processes the Red–Green–Blue (RGB) images and the visible and near-infrared (VIS-NIR) reflectance spectra of each species. The classification results of the CNN model achieved over 94% in all performance metrics, outperforming other state-of-the-art techniques. These results support the potential use of the proposed method to automatically monitor fish landings and, therefore, ensure compliance with the established fishing quotas.

In the pursuit of enhancing the wine production process through the utilization of new technologies in viticulture, this study presents a novel approach for the rapid assessment of wine grape maturity levels using non-destructive, in situ infrared spectroscopy and artificial intelligence techniques. Building upon our previous work focused on estimating sugar content (∘Brix) from the visible and near-infrared (VNIR) and short-wave infrared (SWIR) regions, this research expands its scope to encompass pH and titratable acidity, critical parameters determining the grape maturity degree, and in turn, wine quality, offering a more representative estimation pathway. Data were collected from four grape varieties—Chardonnay, Malagouzia, Sauvignon Blanc, and Syrah—during the 2023 harvest and pre-harvest phenological stages in the vineyards of Ktima Gerovassiliou, northern Greece. A comprehensive spectral library was developed, covering the VNIR–SWIR spectrum (350–2500 nm), with measurements performed in situ. Ground truth data for pH, titratable acidity, and sugar content were obtained using conventional laboratory methods: total soluble solids (TSS) (∘Brix) by refractometry, titratable acidity by titration (expressed as mg tartaric acid per liter of must) and pH by a pH meter, analyzed at different maturation stages in the must samples. The maturity indicators were predicted from the point hyperspectral data by employing machine learning algorithms, including Partial Least Squares regression (PLS), Random Forest regression (RF), Support Vector Regression (SVR), and Convolutional Neural Networks (CNN), in conjunction with various pre-processing techniques. Multi-output models were also considered to simultaneously predict all three indicators to exploit their intercorrelations. A novel multi-input–multi-output CNN model was also proposed, incorporating a multi-head attention mechanism and enabling the identification of the spectral regions it focuses on, and thus having a higher interpretability degree. Our results indicate high accuracy in the estimation of sugar content, pH, and titratable acidity, with the best models yielding mean R2 values of 0.84, 0.76, and 0.79, respectively, across all properties. The multi-output models did not improve the prediction results compared to the best single-output models, and the proposed CNN model was on par with the next best model. The interpretability analysis highlighted that the CNN model focused on spectral regions associated with the presence of sugars (i.e., glucose and fructose) and of the carboxylic acid group. This study underscores the potential of portable spectrometry for real-time, non-destructive assessments of wine grape maturity, thereby providing valuable tools for informed decision making in the wine production industry. By integrating pH and titratable acidity into the analysis, our approach offers a holistic view of grape quality, facilitating more comprehensive and efficient viticultural practices.

Determination of the myristicin and alpha-pinene content of nutmeg is still constrained by the extended testing time in the laboratory, which is expensive and is carried out destructively. In addition, non-destructive testing using spectroscopy often faces problems in building models that only rely on algorithms that perform linearly, such as PCR and PLSR. Therefore, the present study studied Vis-NIR (381-1065 nm) as a fast, inexpensive, and non-destructive mechanism to determine the myristicin and alphapinene of nutmeg fruits from Aceh, Indonesia. Two algorithms commonly used in spectral data processing, partial least squares regression (PLSR) and machine learning represented by a support vector machine (SVM), were employed and compared to predict myristicin and alpha-pinene in nutmeg fruits. The chemical reference parameters (myristicin and alpha-pinene) were measured using gas chromatography mass spectrometry (GC-MS). Standard normal variate (SNV) and multiplicative scatter correction (MSC) preprocessing were involved as spectra enhancement before the prediction models outcome. The results show that the kernel of the radial basis function (RBF) kernel ε-SVM algorithm is better than PLSR for myristicin prediction with gamma (ε), c, and nu (ε) of 0.1, 1.0, and 0.99, respectively. Also, the ε-SVM algorithm by RBF kernel is better than PLSR for the prediction of alpha-pinene in nutmeg fruits with gamma (ε), c, and epsilon (ε) compositions of 0.01, 10, 0.1, respectively. The coefficient correlation of calibration (rc) and coefficient determination of prediction (Rp²), the root means square error of calibration (RMSEC) and prediction (RMSEP), and the ratio (RPD) for the prediction of myristicin were 0.992, 0.986, 0.941%, 1.325% and 8.348, respectively. The coefficient correlation of calibration (rc) and coefficient determination of prediction (Rp²), the root mean square error of calibration (RMSEC) and prediction (RMSEP), and the ratio of prediction to deviation ratio (RPD) for the prediction of alpha-pinene were 0.976, 0.979, 0.305%, 0.317% and 6.826, respectively. In general, the results satisfactorily indicate that Vis-NIRS, with the appropriate algorithm, has promising results in determining myristicin and alpha-pinene on nutmeg from Aceh, Indonesia, as nondestructive measurement. [ABSTRACT FROM AUTHOR]

Visible and near-infrared spectroscopy (VIS-NIRS) is a fast and simple method increasingly used in soil science. This study aimed to investigate VIS-NIRS applicability to predict soil black carbon (BC) content and the method's suitability for rapid BC-level screening. Forty-three soil samples were collected in an agricultural area remaining under strong industrial impact. Soil texture, pH, total nitrogen (Ntot) and total carbon (Ctot), soil organic carbon (SOC), soil organic matter (SOM), and BC were analyzed. Samples were divided into three classes according to BC content (low, medium, and high BC content) and scanned in the 350–2500 nm range. A support vector machine (SVM) was used to develop prediction models of soil properties. Partial least-square with SVM (PLS-SVM) was used to classify samples for screening purposes. Prediction models of soil properties were at best satisfactory (Ntot: R2 = 0.76, RMSECV = 0.59 g kg−1, RPIQ = 0.65), due to large kurtosis and data skewness. The RMSECV were large (16.86 g kg−1 for SOC), presumably due to the limited number of samples available and the wide data spread. Given our results, the VIS-NIRS method seems efficient for classifying soil samples from an industrialized area according to BC content level (training accuracy of 77% and validation accuracy of 81%). [ABSTRACT FROM AUTHOR]

Moisture content and its distribution is a critical parameter in the production of cereal bars. Inappropriate control of this quality parameter can lead to non-conforming products and excess waste on production lines. In the field of hyperspectral imaging, the search for alternative light sources to stabilised-halogen (cheaper and emitting less heat) is a growing need for the application of this technology in industry. This study compares three different illumination systems for moisture prediction in the visible-near infrared (vis-NIR) range (from 400 nm to 1000 nm). The hyperspectral images were acquired using three illumination systems including two halogen-based systems (stabilised-halogen and conventional-halogen) and an LED-based illumination system. The results showed that halogen-based illumination systems combined with a partial least squares model better predicted moisture in bars. Lower accuracies were obtained when the experiment was performed with an LED-based illumination system, which showed double the error of the halogen-based systems. It was concluded that this is a consequence of the information lost in bands appearing above 850 nm that may be revealing information about the moisture in bars since the second overtone of the water O–H is found at 970 nm. The results demonstrate that conventional halogen-based light systems in the vis-NIR range are a promising method for moisture prediction in cereal bars.

Traditional methods for obtaining soil heavy metal content are expensive, inefficient, and limited in monitoring range. In order to meet the needs of soil environmental quality evaluation and health status assessment, visible near-infrared spectroscopy and XRF spectroscopy for monitoring heavy metal content in soil have attracted much attention, because of their rapid, nondestructive, economical, and environmentally friendly features. The use of either of these spectra alone cannot meet the accuracy requirements of traditional measurements, while the synergistic use of the two spectra can further improve the accuracy of monitoring heavy metal lead content in soil. Therefore, this study applied various spectral transformations and preprocessing to vis-NIR and XRF spectra; used the whale optimization algorithm (WOA) and competitive adaptive re-weighted sampling (CARS) algorithms to identify feature spectra; designed a combination variable model (CVM) based on multi-layer spectral data fusion, which improved the spectral preprocessing and spectral feature screening process to increase the efficiency of spectral fusion; and established a quantitative model for soil Pb concentration using partial least squares regression (PLSR). The estimation performance of three spectral fusion strategies, CVM, outer-product analysis (OPA), and Granger-Ramanathan averaging (GRA), was discussed. The results showed that the accuracy and efficiency of the CARS algorithm in the fused spectra estimation model were superior to those of the WOA algorithm, with an average coefficient of determination (R2) value of 0.9226 and an average root mean square error (RMSE) of 0.1984. The accuracy of the estimation models established, based on the different spectral types, to predict the Pb content of the soil was ranked as follows: the CVM model > the XRF spectral model > the vis-NIR spectral model. Within the CVM fusion strategy, the estimation model based on CARS and PLSR (CARS_D1+D2) performed the best, with R2 and RMSE values of 0.9546 and 0.2035, respectively. Among the three spectral fusion strategies, CVM had the highest accuracy, OPA had the smallest errors, and GRA showed a more balanced performance. This study provides technical means for on-site rapid estimation of Pb content based on multi-source spectral fusion and lays the foundation for subsequent research on dynamic, real-time, and large-scale quantitative monitoring of soil heavy metal pollution using high-spectral remote sensing images.

A highly sensitive optical sensor is presented. This sensor is having multilayer structure that is designed and optimized theoretically by using Zinc Sulfide (ZnS) and Polyvinylpyrrolidone (PVP). The optical sensor proposed here is based on Surface Plasmon Resonance (SPR) technique by using Kretschmann configuration. This SPR sensor is designed with BK-7 prism which is coated with silver (Ag), overlayers ZnS and PVP in sequence. Silver is the plasmonic metal, PVP and ZnS are polymer and dielectric respectively and they perform the interaction with analyte and sensitivity enhancement however ZnS contributes more to enhance the sensitivity. For the characterization of multilayer Ag/ZnS/PVP structure of optical sensor, spectral interrogation mode is used. The surface plasmon resonance wavelength was calculated for each concentration. It shifts linearly towards higher wavelength with the increasing concentration of the analytes from 1.33 to 1.35 refractive index when interact with sensing surface. The sensitivity of the sensor is found to be 8537.06 nm/RIU throughout. The spectral range of proposed sensor is Visible-Near Infra Red (VIS-NIR) region from 600 to 1050 nm. The detection accuracy (DA) decreases with increasing concentration of analytes. Figure of merit (FOM) is also having same nature as DA. The sensitivity of the proposed sensor is quite high in comparison to other sensors. The proposed sensor will be useful to sense the chemical and biological compounds within its detection range (from 1.33 to 1.35 refractive index) in VIS-NIR region from experimental point of view.

Calorific value (CV) reflects the ability of material flow and energy conversion of plants, which is the key indices of combustion properties for utilization and development of energy plants. However, the commonly used method for CV determination of solid fuels is bomb calorimetry in the laboratory using powder samples, which hinders the capability of rapid and non-destructive prediction for a large-scale samples in a natural environment. Visible and near infrared spectroscopy (Vis-NIR) has been widely proposed as a replace for laboratory determination in properties prediction. However, chemometrics are essential for spectral analysis. Various chemometrics including competitive adaptive reweighted sample (CARS), lifting wavelet transform (LWT), successive projections algorithm (SPA), and convolutional neural networks (CNNs) optimized by whale optimization algorithm (WOA) were employed to optimize models. Additionally, canopy spectra were measured in the field instead of powder samples' spectra collecting from laboratory. The results demonstrated that CARS-WOA-CNN was the best to predict CV and ash content (AC) with R2 of 0.858 and 0.751, respectively. Compared to raw full spectra, spectral dimension was reduced from 2048 to 93 and 22 for CV and AC, respectively. Overall, this study provided a meaningful strategy for harvest planning and assessing value of biomass in the field. [Display omitted] [ABSTRACT FROM AUTHOR]

The soluble solids content (SSC) is an important indicator for evaluating the internal quality of apples. This study proposed a novel method for detecting apple SSC. This method involved collecting the transmission spectra of apples, transforming one-dimensional spectra into two-dimensional images using the gramian angular difference field (GADF) technique, and analyzing the images using an improved mobile vision transformer (MobileViT) model. Furthermore, the Grad-CAM method was used to visualize the model's prediction effect on SSC and find the wavelength range that had a greater impact on the model results. The results indicated that the GADF-improved MobileViT model exhibited excellent performance on the test dataset, with a determination coefficient (R 2) of 0.938, a root mean square error (RMSE) of 0.532, and a mean absolute error (MAE) of 0.423. As the number of wavelengths used for modeling decreased, the improvements made in this study to MobileViT continued to enhance the model's performance effectively. And when using fewer training samples, the GADF-improved MobileViT model also had better performance than the original model. In conclusion, using visible and near-infrared spectroscopy and GADF image encoding techniques for the prediction of apple SSC is feasible. • The SSC of golden venus apples was predicted by visible/NIR spectroscopy. • Converting 1D spectral data into 2D spectral images to predict apple SSC. • Proposed GADF-improved MobileViT based on transformer to predict apple SSC. • The Grad-CAM visualized influential wavelength ranges impacting model predictions. [ABSTRACT FROM AUTHOR]

Visible‐to‐near‐infrared organic photodetectors (vis–NIR OPDs) are highly desired due to their potential applications in both scientific research and industry. To develop state‐of‐the‐art diode‐type OPDs, general strategies aiming to reduce the dark current density (Jd) involve reducing the thickness of the bulk‐heterojunction (BHJ) active layer, but this simultaneously leads to a sharp drop in photoresponse. Herein, a facile fabrication strategy, i.e., thermally induced anti‐aggregation (TIAA) evolution strategy, is introduced for manipulating the phase separation scale and molecular arrangement orientation of the PBDB‐T:Y6 based active layer. To a typical 500 nm‐thick BHJ, much higher and balanced electron/hole mobilities are achieved through the TIAA manipulation. By effectively equilibrating the Jd (8.7 × 10‐8 A cm‐2) and responsibility (0.5 A W‐1 at 860 nm), the optimized vis–NIR OPD showcases high specific detection of over 1012 Jones (380–940 nm) and capability of faint IR light detection (≈10‐10 W cm‐2 at 850 nm) at −0.5 V bias. Meanwhile, the device displays the ability to monitor pulse signal in real time through photoplethysmography, indicating the potential of TIAA evolution strategy to fabricate high‐performance vis–NIR OPDs for next‐generation wearable health monitoring. [ABSTRACT FROM AUTHOR]