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Soil organic carbon influences several landscape ecological processes, and soils are becoming recognized as a mechanism to mitigate the negative impacts of climate change. There is a need to define methods and technologies for addressing soils' spatial variability as well as the time and cost of sampling soil organic carbon (SOC). Visible and near-infrared spectroscopy have been suggested as a sampling tool to reduce inventory cost. We sampled nineteen ranch properties totaling 17,347 ha across Oklahoma and Texas in 2019 to evaluate the effectiveness and accuracy of a handheld reflectometer (Our Sci, Ann Arbor, MI, USA) (370–940 nm) and existing remote sensing approaches to estimate SOC in semi-arid grazing lands. Our data suggest that the Our Sci Reflectometer estimated soil organic carbon with a precision of approximately (±0.3% SOC); however, it was least accurate at higher carbon concentrations. The Our Sci reflectometer, although consistently accurate at lower SOC concentrations, was still less accurate than a model built using only remote sensing and digital soil map data as predictors. Combining the two data sources was the most accurate means of determining SOC. Our results indicated that the Our Sci handheld Vis-NIR reflectometer tested may have only limited applications for reducing inventory costs at scale. [ABSTRACT FROM AUTHOR]

Physical and chemical analysis of soils with traditional methods usually brings some disadvantages to the environment and economy due to time, cost, and chemical waste outputs. The purpose of this research were to i-) determine the increasingly widespread usage areas of visible near-infrared reflection spectroscopy (Vis-NIR) and portable X-ray fluorescence (pXRF) spectra in soil science, ii-) contribute to the increasing the efficiency of Vis-NIR and pXRF spectra and the areas that can be used in soil science for Vis-NIR and pXRF spectra. The advantages of Vis-NIR and pXRF spectra over traditional physical and chemical analysis methods of soils are that they are environmentally friendly, low cost, no need for any chemical substance, and rapid results without damaging soil samples. Therefore, it is increasingly used in characterizing and predicting soil properties. Vis-NIR and pXRF spectra have been widely used to predict the content of sand, silt and clay, organic carbon, organic matter, lime, moisture content, cation exchange capacity, and pH of soils. The researchers reported that Vis-NIR and pXRF spectra were very successful in their individual and combined use in determining and predicting soil properties. To expand the usage areas of Vis-NIR and pXRF spectra in soil science, it is necessary to develop the method and the calibrations of the devices by making more researches in the developing soils under different main materials, land use, climate, and vegetation. Thus, it is predicted that the success performance in the prediction and characterization of soil properties with Vis-NIR and pXRF spectra will increase.

Tomato-based products are significant components of vegetable consumption. The processing tomato industry is unquestionably in need of a rapid definition method for measuring soluble solids content (SSC) and lycopene content. The objective was to find the best chemometric method for the estimation of SSC and lycopene content from visible and near-infrared (Vis-NIR) absorbance and reflectance data so that they could be determined without the use of chemicals in the process. A total of 326 Vis-NIR absorbance and reflectance spectra and reference measurements were available to calibrate and validate prediction models. The obtained spectra can be manipulated using different preprocessing methods and multivariate data analysis techniques to develop prediction models for these two main quality attributes of tomato fruits. Eight different method combinations were compared in homogenized and intact fruit samples. For SSC prediction, the results showed that the best root mean squared error of cross-validation (RMSECV) originated from raw absorbance (0.58) data and with multiplicative scatter correction (MSC) (0.59) of intact fruit in Vis-NIR, and first derivatives of reflectance (R2 = 0.41) for homogenate in the short-wave infrared (SWIR) region. The best predictive ability for lycopene content of homogenate in the SWIR range (R2 = 0.47; RMSECV = 17.95 mg kg–1) was slightly lower than that of Vis-NIR (R2 = 0.68; 15.07 mg kg–1). This study reports the suitability of two Vis-NIR spectrometers, absorbance/reflectance spectra, preprocessing methods, and partial least square (PLS) regression to predict SSC and lycopene content of intact tomato fruit and its homogenate.

Soil acidification is a major and growing concern in many cropping regions globally. Whilst spatial variability in acidification is a common consideration in the management of soil health and fertility at sub-paddock scale, insufficient focus has been directed toward the identification of this variability. Suitability of portable visible near infrared reflectance (vis–NIR) spectroscopy was assessed in this study as a potential technique to achieve rapid, precise, inexpensive and spatially specific quantification of key soil parameters to inform lime requirements. Spectral fingerprints were taken using a 1 ha grid sampling approach, with four sampling protocols investigated as follows, scans: (i) directly on cleared soil surfaces; (ii) on 0–100 mm undisturbed cores; (iii) on dried 0–100 mm cores; and finally (iv) on dried, ground, sieved and mixed cores. Data was analysed using a partial least squares regression (PLSR) model to identify the strength of linear relationship between reference chemistry data and predictions derived from spectral readings. Lime requirement maps using vis–NIR predictions were then theoretically compared against traditional aggregated sampling patterns while considering the trade-offs between accuracy, economics and agronomy associated with the identification of spatial variability. The vis–NIR measurements demonstrated moderate predictive capabilities in field for determining pH (R2 = 0.3–0.5) and liming requirements (R2 = 0.5–0.6) rapidly at high spatial resolution. Vis–NIR in field mapping techniques, which enable the use of site specific management of soil resources, were found to positively redirect lime resources from alkaline areas toward acidic areas of the paddock, resulting in minimal difference to overall expenditure on lime purchase and potential for increased agronomic benefits over the long-term. Further spectral library development, calibration, and research on in-field sampling methods is recommended. [ABSTRACT FROM AUTHOR]

Introduction/Objective. Diabetic nephropathy leading to end-stage renal disease is a major health problem worldwide. Hemodialysis (HD) treatment is associated with glycemia variations. Diabetic patients on HD might benefit from a non-invasive online glycemia monitoring system. The aim of this study was to assess the glucose concentration from the matrix of the spent dialysate fluid using near-infrared (NIR) spectroscopy. Methods. Blood samples and spent dialysate were collected in the 15th minute of the HD treatment from 15 patients. The spent dialysis fluid was characterized by a NIR spectrometer in the range of 900–1300 nm. In order to apply the artificial neural network (ANN) and train it, the MATLAB NFTOOL program was used. The testing and training of the ANN were executed using the NIR spectrum of the spent dialysis fluid as input, and the glucose concentration as output. Results. A significant correlation in excess of 93% between the NIR spectrum of the spent dialysate and the blood glucose concentration (3–9 mmol/l) was found. Conclusions. NIR spectroscopy is a non-invasive and reliable method of glycemia monitoring which can be used in maintaining HD patients.

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.

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.

Choosing the cacao maturity stage is essential for producing high-quality cacao beans. Identifying indicators of the maturity level of cacao is a complex task because these fruits do not exhibit the characteristics of other fruits during the ripening period. Generally, cacao maturity is determined manually based on the estimated daily harvest date using sensory observation, which is marked by changes in the color of the cacao skin. This is certainly inaccurate because visual assessment is only performed subjectively. This is inaccurate because visual assessment is only performed subjectively, which is not in line with the demands of Industrial Revolution 4.0, which is a fast and accurate technology for sorting cacao. In this study, cacao maturity was identified using visible (350-1000 nm) and shortwave near-infrared spectra (SW-NIR) spectroscopy (1000-1600 nm). Chemometric analysis using principal component analysis-linear discriminant analysis (PCA-LDA) was used to classify cacao maturity. The results showed that SW-NIR spectroscopy yielded better performances with calibration and prediction accuracy of 92,50% and 85% using Savitzky–s 1st derivative (SGD1) spectra compared to Vis-NIR spectroscopy had calibration and prediction accuracies of 90% and 86% using raw spectra for PCA-LDA model.

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 (R2), 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 (R2>0.8, RPIQ≥4), while a good fit was attained for the Chardonnay variety from SVR (R2=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.

The quick identification of heavy metals is of major importance and is beneficial for controlling the fertilizer production process in the fertilizer industries. This work aimed to use visible and near-infrared spectroscopy (Vis-NIR), Boruta, and deep learning to establish rapid heavy metals screening methods. Boruta algorithm was used to extract appropriate wavelengths, and a deep belief network (DBN) was computed to determine the amounts of various heavy metals such as chromium (Cr), cadmium (Cd), lead (Pb), and mercury (Hg) for both the entire and selected wavelengths. To assess the model, coefficient of determination (R2), root mean squared error (RMSE), and residual prediction deviation (RPD) were used to calculate the reliability of the model. The results of the selected wavelengths were excellent and much higher than the full wavelengths with R2p = 0.96, RMSEP = 0.2017 mg kg-1 and RPDpred = 5.0 for Cr; R2p = 0.91, RMSEP = 0.2832 mg kg-1 and RPDpred = 3.4 for Pb; R2p = 0.90, RMSEP = 0.2992 mg kg-1, and RPDpred = 3.3 for Hg. Descent prediction was obtained also for Cd (R2p = 0.87, RMSEP = 0.3435 mg kg-1, and RPDpred = 2.7). To further assess the robustness of the DBN, it was compared with conventional machine learning methods such as support vector machine for regression (SVR), k nearest neighbor (KNN), and partial least squares (PLS). The overall results indicated that the Vis-NIR technique coupled with Boruta and DBN could be reliable and accurate for screening heavy metals in organic fertilizers.

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%).

• Seven different machine learning predictors were used in yield prediction. • Eleven spectral VIs directly related to sugar beet yield were determined. • The irrigation treatments included I 125 , I 100 , I 75 , and I 50. • OSAVI, SAVI, and NDVI had the most significant influence on the yield prediction. • The kNN model showed the most outstanding results among the combinations. Yield prediction is essential for production planning, resource management, and competitive advantages. The use of vegetation indices and machine learning for yield prediction is rapid, practical, and accurate and contributes to the development of digital agriculture. Although many studies have been carried out on yield estimation, the number of studies on sugar beet and irrigation applications is limited. To solve this problem, in this study yield prediction of sugar beet under different irrigation treatments was carried out using spectral reflection-based vegetation indices obtained by proximal measurements and developed machine learning models. The multilayer perceptron (MLP), random forest (RF), k-nearest (kNN), bagging (BAG), support vector regression (SVR), gaussian processes (GP), and CustomNet (CN) predictors were used in yield prediction. The irrigation treatments included I 100 (application of irrigation to increase the available moisture to field capacity by around 45–50% of the water holding capacity at the root depth), I 75 (75% of the water applied in the control group), I 50 (50% of the water applied in the control group), and I 125 (25% of the water applied in the control group). The combinations of vegetation indices were determined using Pearson correlation and principal component analysis. OSAVI, SAVI, and NDVI had the most significant influence on the yield prediction of sugar beet. Among the combinations used, the kNN1 model (including all attributes as input) showed high R2 values of 0.99 for training and 0.64 for testing. In addition, the kNN3 model (including Treatment, NDVI, NWI, and OSAVI as inputs) had R2 values of 0.97 for training and 0.65 for testing, indicating successful outcomes. However, the MLP1 and SVR1 models had lower results with test R2 values of 0.53 and 0.56, respectively. The results demonstrate the successful application of machine learning and vegetation indices for yield prediction. The proposed approaches could encourage agricultural experts and researchers in future work for yield mapping and identification. [ABSTRACT FROM AUTHOR]

The change of volatile organic compounds (VOCs) caused by spoilage bacteria are typical characteristics of seafood decay. This study investigated the freshness of shrimp (Litopenaeus vannamei) using a bi-channel data acquisition based on the colorimetric sensing array (CSA) technique. First, nine color-sensitive dyes were selected to capture VOCs changes during shrimp spoilage. Then, both image and spectral channel data from the CSA were used to predict the total volatile basic nitrogen (TVB-N) content and evaluate its prediction ability, respectively. The original full data from the spectral channel performs better than the image channel. Next, four optimization algorithms are tried to filter spectral feature variables. Finally, the variable combination global analysis-iterative retained information variables (VCPA-IRIV) algorithm combined with a partial least squares (PLS) model was determined for predicting the TVB-N of shrimp, achieving the best precision and robustness performance with the R p 2 , RMSEP, and RPD were 0.9734, 1.54, and 6.14, respectively. Therefore, this study provides a new approach to evaluate shrimp freshness rapidly. [Display omitted] • Bi-channel data acquisition of CSA was first used to predict the TVB-N content of Litopenaeus vannamei. • Nile red dye exhibits sensitivity to changes in VOCs during shrimp spoilage. • The spectral channel has better performance than the image channel. • The VCPA-IRIV-PLS model shows the best prediction ability and robustness with an R p 2 of 0.9734. [ABSTRACT FROM AUTHOR]

Soil organic carbon influences several landscape ecological processes, and soils are becoming recognized as a mechanism to mitigate the negative impacts of climate change. There is a need to define methods and technologies for addressing soils’ spatial variability as well as the time and cost of sampling soil organic carbon (SOC). Visible and near-infrared spectroscopy have been suggested as a sampling tool to reduce inventory cost. We sampled nineteen ranch properties totaling 17,347 ha across Oklahoma and Texas in 2019 to evaluate the effectiveness and accuracy of a handheld reflectometer (Our Sci, Ann Arbor, MI, USA) (370–940 nm) and existing remote sensing approaches to estimate SOC in semi-arid grazing lands. Our data suggest that the Our Sci Reflectometer estimated soil organic carbon with a precision of approximately (±0.3% SOC); however, it was least accurate at higher carbon concentrations. The Our Sci reflectometer, although consistently accurate at lower SOC concentrations, was still less accurate than a model built using only remote sensing and digital soil map data as predictors. Combining the two data sources was the most accurate means of determining SOC. Our results indicated that the Our Sci handheld Vis-NIR reflectometer tested may have only limited applications for reducing inventory costs at scale.

Soil texture is one of the soil properties influencing most physical, chemical, and biological soil processes. Information on soil texture is important to support the agronomic decisions for farm management. The problem is how to provide reliable, fast and inexpensive information of soil texture in numerous soil samples and repeated measurement. The objective of this research was to generate the soil texture map based on laboratory Vis-NIR (Visible - Near Infra-Red) spectroscopy and inverse distance weighted (IDW) interpolation method. An ASD Fieldspec 3 with a spectral range from 350 nm to 2500 nm was used to measure the soil reflectance. Pipette method was used to measure the silt, clay and sand fractions. The partial least square regression (PLSR) was performed to establish the prediction model of soil texture. The predicted values were mapped and showing the information of spatial and temporal variability of soil texture. Keywords: Vis-NIR, spectroscopy, soil texture, PLSR, IDW

Due to its health benefits, resistant starch (RS) has received increasing attention from the public, and there is a need to develop methods to measure the amylose and RS concentration in pea (Pisum sativum L.) flour. The aim of this study was to develop a visible and near-infrared reflectance (vis–NIR) model for the simultaneous determination of amylose, RS, and digestible starch (DS) in pea flour. A total of 123 dry pea samples consisting of different pea varieties grown in different environments were collected, and ground to flour, and then the vis–NIR spectra were scanned. The amylose, RS, and DS contents of the pea flours were also measured by an enzymatic colorimetric assay. The spectra data were calibrated with the enzymatic colorimetric-assayed values. Results showed that amylose, RS, and DS in the pea flours can be simultaneously estimated using the vis–NIR spectra. Instead of using the full spectrum (300–2300 nm), we found the most efficient wave bands lying in the visible region between 370 and 560 nm and the NIR spectra in the range of 1600–1800 nm. Using the stepwise regression with backward elimination method, the multiple linear regression (MLR) models were developed from the most efficient wavelengths. The MLR models had the determination coefficients R2 of 0.95, 0.76, 0.80, and 0.88 for amylose, RS, DS, and total starch, respectively. The correlation coefficients between model estimated and the enzymatic colorimetric assayed values were 0.97, 0.80, 0.85, and 0.93 for amylose, RS, DS, and total starch, respectively.

The necessity to create databases for the proper interpretation of the results obtained by the rovers that will be sent by ESA to Mars implies the study of terrestrial Martian analogues. In this work, the Meñakoz outcrops, which are basaltic volcanic rocks interbedded with Turonian sediments coming from a Cretaceous submarine volcano outcrop located in Biscay (northern Spain), have been studied and proposed as a Martian analogue. Considering that Raman and near infrared spectroscopies are two of the techniques that will go on‐board the rovers in the next planetary missions, they were used to characterize the samples collected in Meñakoz. As was expected, silicates (phyllosilicates) were the major minerals detected by those techniques in the Meñakoz samples, but also, compounds such as sulfates and oxides could be detected. Phyllosilicates are very important because they are some of the compounds that have been reported to be present on the Mars surface in previous missions and specifically in Oxia Planum, the landing site for the ExoMars 2020 rover of the ESA missions to Mars. This fact provides reasons to propose the emplacement of Meñakoz as Martian analogue. [ABSTRACT FROM AUTHOR]

Rapid and accurate identification of contaminated meat products represents a global challenge, particularly with regard to minced meat products. This study reports on two methods for analyzing the adulteration of beef and mutton: the deep learning combined with two-dimensional correlation spectroscopy (2DCOS) method, and the PLS-DA method. The 2DCOS method employed in this research substantially improves the resolution of one-dimensional Vis-NIR spectra, allowing for the visualization of spectral information changes within the samples. By analyzing the impact of various proportions of chicken, duck, and pork mixed with beef or mutton, the synchronous 2DCOS images unveil distinct patterns of chemical information alteration within the spectra under different adulteration scenarios. Notably, the auto peaks and cross peaks observed within the 400–1400 nm range serve as key indicators of these changing patterns. The ResNet deep learning method possesses the advantageous capability to effectively extract 2DCOS feature information, resulting in the achievement of high accuracy models (100%). In contrast, the accuracy of the PLS-DA model test set, based on either raw or pre-processed spectral data matrices, ranged from 32.97% to 50.64%. These results substantiate the effectiveness of utilizing 2DCOS in combination with deep learning as a powerful tool for discerning beef and mutton adulteration. [Display omitted] • 2DCOS enhances the resolution of the original spectrum. • The ResNet model demonstrates proficiency in extracting 2DCOS data. • Synchronous 2DCOS contains information to identify adulteration of meat products. • 2DCOS combined with ResNet accurately identified adulteration of meat products. [ABSTRACT FROM AUTHOR]

The visible and near-infrared (Vis-NIR) reflectance spectroscopy was utilized for the rapid and nondestructive discrimination of edible oil adulteration. In total, 110 samples of sesame oil and rapeseed oil adulterated with soybean oil in different levels were produced to obtain the reflectance spectra of 350–2500 nm. A set of multivariant methods was applied to identify adulteration types and adulteration rates. In the qualitative analysis of adulteration type, the support vector machine (SVM) method yielded high overall accuracy with multiple spectra pretreatments. In the quantitative analysis of adulteration rate, the random forest (RF) combined with multivariate scattering correction (MSC) achieved the highest identification accuracy of adulteration rate with the full wavelengths of Vis-NIR spectra. The effective wavelengths of the Vis-NIR spectra were screened to improve the robustness of the multivariant methods. The analysis results suggested that the competitive adaptive reweighted sampling (CARS) was helpful for removing the redundant information from the spectral data and improving the prediction accuracy. The PLSR + MSC + CARS model achieved the best prediction performance in the two adulteration cases of sesame oil and rapeseed oil. The coefficient of determination (RPcv2) and the root mean square error (RMSEPcv) of the prediction set were 0.99656 and 0.01832 in sesame oil adulterated with soybean oil, and the RPcv2 and RMSEPcv were 0.99675 and 0.01685 in rapeseed oil adulterated with soybean oil, respectively. The Vis-NIR reflectance spectroscopy with the assistance of multivariant analysis can effectively discriminate the different adulteration rates of edible oils.

Organic fertilizer is a key component of agricultural sustainability and significantly contributes to the improvement of soil fertility. The values of nutrients such as organic matter and nitrogen in organic fertilizers positively affect plant growth and cause environmental problems when used in large amounts. Hence the importance of implementing fast detection of nitrogen (N) and organic matter (OM). This paper examines the feasibility of a framework that combined a particle swarm optimization (PSO) and two multiple stacked generalizations to determine the amount of nitrogen and organic matter in organic-fertilizer using visible near-infrared spectroscopy (Vis-NIR). The first multiple stacked generalizations for classification coupled with PSO (FSGC-PSO) were for feature selection purposes, while the second stacked generalizations for regression (SSGR) improved the detection of nitrogen and organic matter. The computation of root means square error (RMSE) and the coefficient of determination for calibration and prediction set (R2) was used to gauge the different models. The obtained FSGC-PSO subset combined with SSGR achieved significantly better prediction results than conventional methods such as Ridge, support vector machine (SVM), and partial least square (PLS) for both nitrogen (R2p = 0.9989, root mean square error of prediction (RMSEP) = 0.031 and limit of detection (LOD) = 2.97) and organic matter (R2p = 0.9972, RMSEP = 0.051 and LOD = 2.97). Therefore, our settled approach can be implemented as a promising way to monitor and evaluate the amount of N and OM in organic fertilizer.

Soil salinization is the primary obstacle to the sustainable development of agriculture and eco-environment in arid regions. The accurate inversion of the major water-soluble salt ions in the soil using visible-near infrared (VIS-NIR) spectroscopy technique can enhance the effectiveness of saline soil management. However, the accuracy of spectral models of soil salt ions turns out to be affected by high dimensionality and noise information of spectral data. This study aims to improve the model accuracy by optimizing the spectral models based on the exploration of the sensitive spectral intervals of different salt ions. To this end, 120 soil samples were collected from Shahaoqu Irrigation Area in Inner Mongolia, China. After determining the raw reflectance spectrum and content of salt ions in the lab, the spectral data were pre-treated by standard normal variable (SNV). Subsequently the sensitive spectral intervals of each ion were selected using methods of gray correlation (GC), stepwise regression (SR) and variable importance in projection (VIP). Finally, the performance of both models of partial least squares regression (PLSR) and support vector regression (SVR) was investigated on the basis of the sensitive spectral intervals. The results indicated that the model accuracy based on the sensitive spectral intervals selected using different analytical methods turned out to be different: VIP was the highest, SR came next and GC was the lowest. The optimal inversion models of different ions were different. In general, both PLSR and SVR had achieved satisfactory model accuracy, but PLSR outperformed SVR in the forecasting effects. Great difference existed among the optimal inversion accuracy of different ions: the predicative accuracy of Ca2+, Na+, Cl−, Mg2+ and SO42− was very high, that of CO32− was high and K+ was relatively lower, but HCO3− failed to have any predicative power. These findings provide a new approach for the optimization of the spectral model of water-soluble salt ions and improvement of its predicative precision.

Proximal sensing offers a novel means for determination of the heavy metal concentration in soil, facilitating low cost and rapid analysis over large areas. In this respect, spectral data and model variables play an important role. Thus far, no attempts have been made to estimate soil heavy metal content using continuum-removal (CR), different preprocessing and statistical methods, and different modeling variables. Considering the adsorption and retention of heavy metals in spectrally active constituents in soil, this study proposes a method for determining low heavy metal concentrations in soil using spectral bands associated with soil organic matter (SOM) and visible–near-infrared (Vis–NIR). To rapidly determine the concentration of heavy metals using hyperspectral data, partial least squares regression (PLSR), principal component regression (PCR), and support vector machine regression (SVMR) statistical methods and 16 preprocessing combinations were developed and explored to determine an optimal combination. The results showed that the multiplicative scatter correction and standard normal variate preprocessing methods evaluated with the second derivative spectral transformation method could accurately determine soil Cr and Ni concentrations. The root-mean-square error (RMSE) values of Vis–NIR model combinations with PLSR, PCR, and SVMR were 0.34, 3.42, and 2.15 for Cr, and 0.07, 1.78, and 1.14 for Ni, respectively. Soil Cr and Ni showed strong spectral responses to the Vis–NIR spectral band. The R2 value of the Vis–NIR-based PLSR model was higher than 0.99, and the RMSE value was 0.07–0.34, suggesting higher stability and accuracy. The results were more accurate for Ni than Cr, and PLSR showed the best performance, followed by SVMR and PCR. This perspective has critical implications for guiding quantitative biogeochemical analysis using proximal sensing data.

Environmental risk of heavy metals (HMs) in soil is commonly assessed by the different risk screening values of HMs under different pH based on soil environmental quality standards. To explore and establish a reliable, rapid and cost-effective method for detailed soil environmental quality survey with high-density sampling in the large-scale area is of significance for theoretical and practical research. In present study, using data from Yunnan Province, China, rapid analysis of soil HMs were conducted via portable X-ray fluorescence (PXRF) spectrometry, and that of soil pH was estimated by applying PXRF and visible near-infrared reflectance (Vis-NIR) spectroscopy data to partial least-squares regression (PLSR) and support vector machine regression (SVMR). Then we compared soil HM contamination grades calculated by conventional laboratory analysis data with those of rapid analysis data. It was found that soil HMs (i.e., As, Pb, Cu, and Zn) were successfully estimated by PXRF with high coefficient of determination (R 2) above 0.97 (P < 0.001). SVMR with fused sensor dataset (here PXRF and Vis-NIR) provided the best predictive model for soil pH estimation (R 2 = 0.86; the ratio of performance to deviation (RPD) = 2.21; the ratio of performance to interquartile distance (RPIQ) = 3.09). The Kappa coefficient of the classification was 0.91, a very high-level consistency between the assessment of soil HM contamination grades calculated by rapid analysis data and that of conventional laboratory analysis data. Therefore, our study suggested a promising method to rapidly detect soil HM contamination under different pH intervals, which would considerably reduce the financial burden of detailed soil HM survey great sampling number in large-scale areas. [ABSTRACT FROM AUTHOR]

Soil salinization is one of the most common forms of land degradation. The detection and assessment of soil salinity is critical for the prevention of environmental deterioration especially in arid and semi-arid areas. This study introduced the fractional derivative in the pretreatment of visible and near infrared (VIS–NIR) spectroscopy. The soil samples (n = 400) collected from the Ebinur Lake Wetland, Xinjiang Uyghur Autonomous Region (XUAR), China, were used as the dataset. After measuring the spectral reflectance and salinity in the laboratory, the raw spectral reflectance was preprocessed by means of the absorbance and the fractional derivative order in the range of 0.0–2.0 order with an interval of 0.1. Two different modeling methods, namely, partial least squares regression (PLSR) and random forest (RF) with preprocessed reflectance were used for quantifying soil salinity. The results showed that more spectral characteristics were refined for the spectrum reflectance treated via fractional derivative. The validation accuracies showed that RF models performed better than those of PLSR. The most effective model was established based on RF with the 1.5 order derivative of absorbance with the optimal values of R2 (0.93), RMSE (4.57 dS m−1), and RPD (2.78 ≥ 2.50). The developed RF model was stable and accurate in the application of spectral reflectance for determining the soil salinity of the Ebinur Lake wetland. The pretreatment of fractional derivative could be useful for monitoring multiple soil parameters with higher accuracy, which could effectively help to analyze the soil salinity. [ABSTRACT FROM AUTHOR]

Current methods for detecting aflatoxin contamination of agricultural and food commodities are generally based on wet chemical analyses, which are time-consuming, destructive to test samples, and require skilled personnel to perform, making them impossible for large-scale nondestructive screening and on-site detection. In this study, we utilized visible–near-infrared (Vis-NIR) spectroscopy over the spectral range of 400–2500 nm to detect contamination of commercial, shelled peanut kernels (runner type) with the predominant aflatoxin B1 (AFB1). The artificially contaminated samples were prepared by dropping known amounts of aflatoxin standard dissolved in 50:50 (v/v) methanol/water onto peanut kernel surface to achieve different contamination levels. The partial least squares discriminant analysis (PLS-DA) models established using the full spectra over different ranges achieved good prediction results. The best overall accuracy of 88.57% and 92.86% were obtained using the full spectra when taking 20 and 100 parts per billion (ppb), respectively, as the classification threshold. The random frog (RF) algorithm was used to find the optimal characteristic wavelengths for identifying the surface AFB1-contamination of peanut kernels. Using the optimal spectral variables determined by the RF algorithm, the simplified RF-PLS-DA classification models were established. The better RF-PLS-DA models attained the overall accuracies of 90.00% and 94.29% with the 20 ppb and 100 ppb thresholds, respectively, which were improved compared to using the full spectral variables. Compared to using the full spectral variables, the employed spectral variables of the simplified RF-PLS-DA models were decreased by at least 94.82%. The present study demonstrated that the Vis-NIR spectroscopic technique combined with appropriate chemometric methods could be useful in identifying AFB1 contamination of peanut kernels. [ABSTRACT FROM AUTHOR]

C3N4‐decorated carbon‐fiber (CF) cloth has been demonstrated to be a filter‐membrane‐shaped photocatalyst for degrading flowing wastewater, but it can be excited only by visible light (λ<450 nm). To extend the photoresponse range to NIR region, herein we have designed and prepared C3N4−Ag−Ag2O heterojunctions on CF cloth. The growth of C3N4 nanosheets (thickness: 20–30 nm) on CF cloth is realized by a thermal condensation method, and then Ag/Ag2O nanoparticles (10–20 nm) are deposited on CF/C3N4 cloth by a chemical precipitation method. CF/C3N4/Ag/Ag2O cloth exhibits excellent flexibility and strong photoabsorption in a broad Vis‐NIR region (400–1400 nm), wider than CF/C3N4 (absorption edge: 450 nm). Under the irradiation of 808 nm or 980 nm laser, CF/C3N4/Ag/Ag2O cloth can remove 83 %/41 % AO7 and 72 %/38 % 4‐CP in 140 min respectively, significantly higher than those (33 %/13 % AO7 and 31 %/10 % 4‐CP) by CF/Ag/Ag2O cloth and those (3 %/1 % AO7 and 0 %/1 % 4‐CP) by CF/C3N4 cloth. Interestingly, when CF/C3N4/Ag/Ag2O cloth is used as the filter‐membrane to photo‐degrade the flowing wastewater (AO7, 20 mg L−1, rate: 1.5 L h−1), the removal efficiency of AO7 reaches up 97 % after 8 grade reactors. Therefore, CF/C3N4/Ag/Ag2O cloth can be used as an efficient filter‐membrane‐shaped photocatalyst with NIR photoresponse. At a junction! The growth of C3N4/Ag/Ag2O semiconductor heterojunctions on carbon fiber cloth was realized by a thermal condensation and chemical precipitation two‐step method. CF/C3N4/Ag/Ag2O cloth exhibits excellent flexibility and strong photoabsorption in a broad Vis‐NIR region (400–1400 nm). Under the irradiation of visible light, 808 nm or 980 nm laser, CF/C3N4/Ag/Ag2O cloth displays enhanced photocatalytic activity compared with CF/C3N4 cloth or CF/Ag/Ag2O cloth. [ABSTRACT FROM AUTHOR]

Abstract Microplastic accumulation in soil may have a detrimental impact on soil biota. The lack of standardized methods to identify and quantify microplastics in soils is an obstacle to research. Existing techniques are time-consuming and field data are seldom collected. To tackle the problem, we explored the possibilities of using a portable spectroradiometer working in the near infrared range (350–2500 nm) to rapidly assess microplastic concentrations in soils without extraction. Four sets of artificially polluted soil samples were prepared. Three sets had only one polymer polluting the soil (low-density polyethylene (LDPE), polyethylene terephthalate (PET), or polyvinyl chloride (PVC)). The fourth set contained random amounts of the three polymers (Mix). The concentrations of microplastics were regressed on the reflectance observed for each of the 2150 wavelengths registered by the instrument, using a Bayesian approach. For a measurement range between 1 and 100 g kg−1, results showed a root-mean-squared-deviation (RMSD) of 8, 18, and 10 g kg−1 for LDPE, PET, and PVC. The Mix treatment presented an RMSD of 8, 10, and 5 g kg−1 for LDPE, PET, and PVC. The repeatability of the proposed method was 0.2–8.4, 0.1–5.1, and 0.1–9.0 g kg−1 for LDPE, PET, and PVC, respectively. Overall, our results suggest that vis-NIR techniques are suitable to identify and quantify LDPE, PET, and PVC microplastics in soil samples, with a 10 g kg−1 accuracy and a detection limit ≈ 15 g kg−1. The method proposed is different than other approaches since it is faster because it avoids extraction steps and can directly quantify the amount of plastic in a sample. Nevertheless, it seems to be useful only for pollution hotspots. Graphical abstract Unlabelled Image Highlights • Methods to quantify and qualify microplastics in soils are slow and unstandardized. • The spectra of a soil with microplastics were characterized with a spectrometer. • The spectra was used to train a model for microplastic analysis in soils. • The method was useful to quantify and qualify LDPE, PET, and PVC in soil samples. • Fast methods to analyse microplastics could be developed using the same technique. [ABSTRACT FROM AUTHOR]

Soil organic carbon (C) is an important indicator of agricultural and environmental quality. It improves soil fertility and helps to mitigate greenhouse gas emissions. Soil spectroscopy with either vis–NIR (350–2500 nm) or mid-IR (4000–400 cm −1 ) spectra have been used successfully to predict organic C concentrations in soil. However, research to improve predictions of soil organic C by simply combining vis–NIR and mid-IR spectra to model them together has been unsuccessful. Here we use the Outer Product Analysis (OPA) to fuse vis–NIR and mid-IR spectra by bringing them into a common spectral domain. Using the fused data, we derived models to predict soil organic C and compared its predictions to those derived with vis–NIR and mid-IR models separately. We analyzed 1259 tropical soil samples from surface and subsurface layers across agricultural areas in Central Brazil. Soil organic C contents were determined by a modified Walkley-Black method, and vis–NIR and mid-IR reflectance spectra were obtained with a FieldSpec Pro and a Nicolet 6700 Fourier Transformed Infrared (FT-IR), respectively. Reflectances were log-transformed into absorbances. The mean content of soil organic C was 9.14 g kg −1 (SD = 5.64 g kg −1 ). The OPA algorithm was used to emphasize co-evolutions of each spectral domain into the same one by multiplying the absorbances from both sets of spectra to produce a matrix with all possible products between them. Support Vector Machine with linear kernel function was used for the spectroscopic modeling. Predictions of soil organic C using vis–NIR, mid-IR, and fused spectra were statistically compared by the Tukey's test using the coefficient of determination (R 2 ), root mean squared error (RMSE), and ratio of performance to interquartile distance (RPIQ). Absorbances in vis–NIR and mid-IR were emphasized in the common spectral domain presenting stronger correlations with soil organic C than individual ranges. Soil organic C predictions with the OPA fused spectra were significantly better (R 2  = 0.81, RMSE = 2.42 g kg −1 , and RPIQ = 2.87) than those with vis–NIR (R 2  = 0.69, RMSE = 3.38 g kg −1 , and RPIQ = 2.08) or mid-IR spectra (R 2  = 0.77, RMSE = 2.90 g kg −1 , and RPIQ = 2.43). Fusing vis–NIR and mid-IR spectra by OPA improves predictions of soil organic C. [ABSTRACT FROM AUTHOR]

Total volatile basic nitrogen (TVB-N) content is one of the critical safety evaluation indexes in seafood. This study investigated the colorimetric sensing array (CSA) technique with two data collection blocks, imaging and visible near-infrared spectroscopy, for non-destructive prediction of TVB-N content in Fujian oysters. Three data fusion strategies coupled with variable selection approaches including variable combination population analysis (VCPA), ant colony optimization, and competitive adaptive reweighted sampling were applied to establish the partial least squares (PLS) model for predicting the TVB-N content of oysters. Better prediction performance was obtained using data fusion of two information blocks than using individual block data. The results showed that the high-level-VCPA strategy showed the best performance with the correlation coefficient of the prediction set (R p) at 0.9128. This work demonstrates that the use of CSA has the potential for non-destructive monitoring of TVB-N content in Fujian oysters. • Predicted the TVB-N content of Fujian oysters by integrating CSA image and Vis-NIR. • A novel data fusion strategy is used in this study. • The high-level-VCPA strategy showed the best performance with R p of 0.9128. [ABSTRACT FROM AUTHOR]

• Soil EC can be predicted by Vis-NIR and smartphone color coordinates. • Individual or combined Vis-NIR and pXRF had the best prediction accuracy. • GRU is more accurate than SVR and RF models to predict soil EC. • CNN is more accurate than SVR and RF models for smartphone-based digital images. The soil EC was predicted using smartphone-based (iPhone 11) color coordinates (RGB, HSV, and CIE L* a * b *) and individual and combined Vis-NIR and pXRF spectra. Prediction models used were: one-dimension (1D) data-driven machine learning and recurrent neural network (RNN), and two-dimension (2D) convolutional neural network (CNN). A total of 240 soil samples were collected from 0 to 20 cm depth and air-dried samples were used for smartphone-based digital images and spectra (Vis-NIR and pXRF). Most smartphone-based and Vis-based color coordinates could be used to predict EC and salinity classes. Combined Vis-NIR and pXRF spectra had the highest prediction accuracy (R2 = 0.93) for predicting EC compared to individual Vis-NIR or pXRF spectra and smartphone-based and Vis-based color coordinates. We conclude that smartphone-based digital images based on the 2D-CNN model can be used to predict EC, but we recommend using combined Vis-NIR + pXRF spectra with gated recurrent unit (GRU) model for the highest prediction accuracy. [ABSTRACT FROM AUTHOR]

Ganodermaboninense (G. boninense) is a fungus that causes one of the most destructive diseases in oil palm plantations in Southeast Asia called basal stem rot (BSR), resulting in annual losses of up to USD 500 million. The G. boninense infects both mature trees and seedlings. The current practice of detection still depends on manual inspection by a human expert every two weeks. This study aimed to detect early G. boninense infections using visible-near infrared (VIS-NIR) hyperspectral images where there are no BSR symptoms present. Twenty-eight samples of oil palm seedlings at five months old were used whereby 15 of them were inoculated with the G. boninense pathogen. Five months later, spectral reflectance oil palm leaflets taken from fronds 1 (F1) and 2 (F2) were obtained from the VIS-NIR hyperspectral images. The significant bands were identified based on the high separation between uninoculated (U) and inoculated (I) seedlings. The results indicate that the differences were evidently seen in the NIR spectrum. The bands were later used as input parameters for the development of Support Vector Machine (SVM) classification models, and these bands were optimized according to the classification accuracy achieved by the classifiers. It was observed that the U and I seedlings were excellently classified with 100% accuracy using 35 bands and 18 bands of F1. However, the combination of F1 and F2 (F12) gave better accuracy than F2 and almost similar to F1 for specific classifiers. This finding will provide an advantage when using aerial images where there is no need to separate F1 and F2 during the data pre-processing stage.

‘Fuji’ is among the most cultivated apples worldwide but affected by the disorder bitter pit (BP). Calcium deficiency plays an important role on fruit susceptibility to BP. The objectives of this study were to determine nutritional relationships in BP-affected fruit and to verify if Vis-NIR models can predict Ca concentration in ‘Fuji’ apples. Fruit was harvested during 2018 season from two different orchards with historical high BP incidence. Seven hundred and fifty apples were stored at 0 °C for 150 days plus 10 days at 20 °C for BP assessments. After storage, 20 fruit with BP symptoms (BP+) and 20 healthy fruit (BP−) were assessed individually for mineral concentration. Vis-NIR evaluation involved a spectra range from 285 to 1200 nm to predict Ca concentration from ‘Fuji’ powder enriched Ca solutions. In each orchard, healthy apples had significantly higher Ca concentration than apples with BP. The K/Ca and Mg/Ca ratios were significantly lower in healthy fruit compared with BP− affected fruit. The relationship B/Ca proved to be significant in BP fruit. Although Ca interaction with organic substances and/or cellular structures could influence NIR spectra in fresh fruit, our results showed that Vis-NIR models could not be used to direct prediction of fruit Ca concentration.

Extra virgin olive oil (EVOO) represents a crucial ingredient of the Mediterranean diet. Being a first-choice product, consumers should be guaranteed its quality and geographical origin, justifying the high purchasing cost. For this reason, it is important to have new reliable tools able to classify products according to their geographical origin. The aim of this work was to demonstrate the efficiency of an open source visible and near infra-red (VIS-NIR) spectrophotometer, relying on a specific app, in assessing olive oil geographical origin. Thus, 67 Italian and 25 foreign EVOO samples were analyzed and their spectral data were processed through an artificial intelligence algorithm. The multivariate analysis of variance (MANOVA) results reported significant differences (p < 0.001) between the Italian and foreign EVOO VIS-NIR matrices. The artificial neural network (ANN) model with an external test showed a correct classification percentage equal to 94.6%. Both the MANOVA and ANN tested methods showed the most important spectral wavelengths ranges for origin determination to be 308–373 nm and 594–605 nm. These are related to the absorption of phenolic components, carotenoids, chlorophylls, and anthocyanins. The proposed tool allows the assessment of EVOO samples’ origin and thus could help to preserve the “Made in Italy” from fraud and sophistication related to its commerce.

Wise soil management requires detailed soil information, but conventional soil class mapping in a rather coarse spatial resolution cannot meet the demand for precision agriculture. With the advantages of non-destructiveness, rapid cost-efficiency, and labor savings, the spectroscopic technique has proved its high potential for success in soil classification. Previous studies mainly focused on predicting soil classes using a single sensor. In this study, we attempted to compare the predictive ability of visible near infrared (vis-NIR) spectra, mid-infrared (MIR) spectra, and their fused spectra for soil classification. A total of 146 soil profiles were collected from Zhejiang, China, and the soil properties and spectra were measured by their genetic horizons. Along with easy-to-measure auxiliary soil information (soil organic matter, soil texture, color and pH), four spectral data, including vis-NIR, MIR, their simple combination (vis-NIR-MIR), and outer product analysis (OPA) fused spectra, were used for soil classification using a multiple objectives mixed support vector machine model. The independent validation results showed that the classification model using MIR (accuracy of 64.5%) was slightly better than that using vis-NIR (accuracy of 64.2%). The predictive model built on vis-NIR-MIR did not improve the classification accuracy, having the lowest accuracy of 61.1%, which likely resulted from an over-fitting problem. The model based on OPA fused spectra performed best with an accuracy of 68.4%. Our results prove the potential of fusing vis-NIR and MIR using OPA for improving prediction ability for soil classification.

This study aimed to assess the feasibility of visible/near-infrared reflectance (Vis-NIR) and near-infrared transmittance (NIT) spectroscopy to predict total and gelatinized starch and fiber fractions in extruded dry dog food. Reference laboratory analyses were performed on 81 samples, and the spectrum of each ground sample was obtained through Vis-NIR and NIT spectrometers. Prediction equations for each instrument were developed by modified partial least squares regressions and validated by cross- (CrV) and external validation (ExV) procedures. All studied traits were better predicted by Vis-NIR than NIT spectroscopy. With Vis-NIR, excellent prediction models were obtained for total starch (residual predictive deviation; RPDCrV = 6.33; RPDExV = 4.43), gelatinized starch (RPDCrV = 4.62; RPDExV = 4.36), neutral detergent fiber (NDF; RPDCrV = 3.93; RPDExV = 4.31), and acid detergent fiber (ADF; RPDCrV = 5.80; RPDExV = 5.67). With NIT, RPDCrV ranged from 1.75 (ADF) to 2.61 (acid detergent lignin, ADL) and RPDExV from 1.71 (ADL) to 2.16 (total starch). In conclusion, results of the present study demonstrated the feasibility of at-line Vis-NIR spectroscopy in predicting total and gelatinized starch, NDF, and ADF, with lower accuracy for ADL, whereas results do not support the applicability of NIT spectroscopy to predict those traits.

Soil salinization is one of the most common forms of land degradation. The detection and assessment of soil salinity is critical for the prevention of environmental deterioration especially in arid and semi-arid areas. This study introduced the fractional derivative in the pretreatment of visible and near infrared (VIS–NIR) spectroscopy. The soil samples (n = 400) collected from the Ebinur Lake Wetland, Xinjiang Uyghur Autonomous Region (XUAR), China, were used as the dataset. After measuring the spectral reflectance and salinity in the laboratory, the raw spectral reflectance was preprocessed by means of the absorbance and the fractional derivative order in the range of 0.0–2.0 order with an interval of 0.1. Two different modeling methods, namely, partial least squares regression (PLSR) and random forest (RF) with preprocessed reflectance were used for quantifying soil salinity. The results showed that more spectral characteristics were refined for the spectrum reflectance treated via fractional derivative. The validation accuracies showed that RF models performed better than those of PLSR. The most effective model was established based on RF with the 1.5 order derivative of absorbance with the optimal values of R2 (0.93), RMSE (4.57 dS m−1), and RPD (2.78 ≥ 2.50). The developed RF model was stable and accurate in the application of spectral reflectance for determining the soil salinity of the Ebinur Lake wetland. The pretreatment of fractional derivative could be useful for monitoring multiple soil parameters with higher accuracy, which could effectively help to analyze the soil salinity.

Ground bearing capacity has become a relevant concept for site-specific management that aims to protect soil from the compaction and the rutting produced by the indiscriminate use of agricultural and forestry machines. Nevertheless, commonly known techniques for its estimation are cumbersome and time-consuming. In order to alleviate these difficulties, this paper introduces an innovative sensory system based on Visible-Near InfraRed (VIS-NIR), Short-Wave InfraRed (SWIR) and Long-Wave InfraRed (LWIR) imagery and a sequential algorithm that combines a registration procedure, a multi-class SVM classifier, a K-means clustering and a linear regression for estimating the ground bearing capacity. To evaluate the feasibility and capabilities of the presented approach, several experimental tests were carried out in a sandy-loam terrain. The proposed solution offers notable benefits such as its non-invasiveness to the soil, its spatial coverage without the need for exhaustive manual measurements and its real time operation. Therefore, it can be very useful in decision making processes that tend to reduce ground damage during agricultural and forestry operations.

The objective of this study was to develop calibration models for prediction of moisture content and textural characteristics (fracture force, hardness, apparent modulus of elasticity and compressive energy) of pistachio kernels roasted in different conditions (temperatures 90, 120 and 150 °C; times 20, 35 and 50 min and air velocities 0.5, 1.5 and 2.5 m/s) using Vis/NIR hyperspectral imaging and multivariate analysis. The effects of different pre-processing methods and spectral treatments such as normalization [multiplicative scatter correction (MSC), standard normal variate transformation (SNV)], smoothing (median filter, Savitzky-Golay and Wavelet) and differentiation (first derivative, D1 and second derivative, D2) on the obtained data were investigated. The prediction models were developed by partial least square regression (PLSR) and artificial neural network (ANN). The results indicated that ANN models have higher potential to predict moisture content and textural characteristics of roasted pistachio kernels comparing to PLSR models. High correlation was observed between reflectance data and fracture force (R2 = 0.957 and RMSEP = 3.386) using MSC, Savitzky-Golay and D1, compressive energy (R2 = 0.907 and RMSEP = 15.757) using the combination of MSC, Wavelet and D1, moisture content (R2 = 0.907 and RMSEP = 0.179) and apparent modulus of elasticity (R2 = 0.921 and RMSEP = 2.366) employing combination of SNV, Wavelet and D1, respectively. Moreover, Vis-NIR data correlated well with hardness (R2 = 0.876 and RMSEP = 5.216) using SNV, Wavelet and D2. These results showed the capability of Vis/NIR hyperspectral imaging and the central role of multivariate analysis in developing accurate models for prediction of moisture content and textural properties of roasted pistachio kernels. [ABSTRACT FROM AUTHOR]

Redox interaction of reduced technetium forms and technetium(VII) with neptunium(III), neptunium(IV) and neptunium(VI) have been investigated using electrochemical and spectroscopic (Vis-NIR) techniques. The neptunium species most stable in 4 M H2SO4, i.e. Np(IV) ions, do not reduce Tc(VII) in contrast to Np(VI) ions which oxidize Tc(IV) species to Tc(VII). The interaction of pertechnetates with Np(III) leads to formation of Tc(IV) species. The Vis-NIR measurements showed the generation of intermediate Tc(V) and Np(V) forms during the oxidation of Tc(IV) and competitive reduction of Np(VI). Tc(V) and Np(V) forms are characterised by the bands at 460 and 980 nm respectively. [ABSTRACT FROM AUTHOR]

Vitamin C and total acidity were determined in industrialized fruit nectar and soy juices through visible-near infrared (Vis-NIR) spectroscopy and multiproduct, multicomponent, and multivariate calibration, based on partial least squares (PLS2) regression. Since samples with different types, flavors, and sugar content (light or not) were together in the model construction, the samples present higher heterogeneity and it was necessary to optimize the calibration and validation sets by outlier elimination based on leverage and unmodelled residuals in spectral data. The model was developed and validated by the evaluation of the parameters of merit such as accuracy, analytical sensitivity, adjust, linearity, residual prediction deviation, limits of detection, and quantification. The results achieved indicates that the multiproduct, multicomponent, and multivariate calibration model developed from Vis-NIR spectroscopy and PLS2 regression can be used in the industrial routine analysis as an alternative to titration reference methods that are time- and reagent-consuming methods, making the methodology extremely attractive from the industrial point of view. [ABSTRACT FROM AUTHOR]

In order to characterize iron–chromium oxides generated by laser irradiation on the surface of stainless steel plates, an ultraviolet–visible (UV-Vis) near-infrared (NIR) multiwavelength excitation Raman analysis has been performed using both austenitic SS304 and ferritic SS430 stainless steel samples. Raman spectra were obtained using five different excitation wavelengths from blue (455 nm) to NIR (830 nm). These measurements have allowed us to observe and identify four Raman bands, among which two have not been previously observed for iron–chromium oxides, and characterize the existence of different resonant excitation conditions for the different excitation wavelengths. For example, when using 455 nm as excitation wavelength the band at 485 cm−1 did not show up, although that when using 830 nm as excitation wavelength is a clear characteristic band for iron–chromium oxide. In addition, the dependence of the spectra profile with the excitation wavelength for films and microspheres features was observed. This experimental Raman analysis shows the importance of the excitation wavelength for the characterization of metallic oxides with different features. [ABSTRACT FROM AUTHOR]

In this paper, a double-layer coating with both redox and plasmonic electrochromic properties has been designed and deposited on fluorine doped tin oxide (FTO) substrates by sol-gel method. This coating consists of an amorphous WO3 film and a nanocrystalline indium tin oxide (ITO) film. Both the valence of W atoms in WO3 and the localized surface plasmon resonance (LSPR) frequency of ITO nanocrystals can be tuned reversibly and persistently by electrochemical process. Due to the LSPR electrochromism, the double-layer films can block near-infrared (NIR) and visible (Vis) light selectively and independently by applying different voltages, which is better and more functional than the traditional electrochromic films. High transmittance of WO3/ITO films is obtained both in Vis and NIR for positive applied voltages. When a weakly negative voltage is applied, the transmittance of WO3/ITO films in visible region is still high, while the transmittance in NIR decreases. The transmittance of the double-layer films in both Vis and NIR further decreases for increasingly more negative voltages. [ABSTRACT FROM AUTHOR]