Visible And Near-infrared Spectroscopy Research Articles

In-line Application of Visible and Near-Infrared Diffuse Reflectance Spectroscopy to Identify Apple Varieties

One of the most studied techniques for the non-destructive determination of the internal quality of fruits has been visible and near-infrared (VIS-NIR) reflectance spectroscopy. This work evaluates a new non-destructive in-line VIS-NIR spectroscopy prototype for in-line identification of five apple varieties, with the advantage that it allows the spectra to be captured with the probe at the same distance from all the fruits regardless of their size. The prototype was tested using varieties with a similar appearance by acquiring the diffuse reflectance spectrum of the fruits travelling on the conveyor belt at a speed of 0.81 m/s which is nearly 1 fruit/s. Principal component analysis (PCA) was used to determine the variables that explain the most variance in the spectra. Seven principal components were then used to perform linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA). QDA was found to be the best in-line classification method, achieving 98% and 85% success rates for red and yellow apple varieties, respectively. The results indicated that the in-line application of VIS-NIR spectroscopy that was developed is potentially feasible for the detection of apple varieties with an accuracy that is similar to or better than a laboratory system.

Predicting bioavailability change of complex chemical mixtures in contaminated soils using visible and near-infrared spectroscopy and random forest regression

A number of studies have shown that visible and near infrared spectroscopy (VIS-NIRS) offers a rapid on-site measurement tool for the determination of total contaminant concentration of petroleum hydrocarbons compounds (PHC), heavy metals and metalloids (HM) in soil. However none of them have yet assessed the feasibility of using VIS-NIRS coupled to random forest (RF) regression for determining both the total and bioavailable concentrations of complex chemical mixtures. Results showed that the predictions of the total concentrations of polycyclic aromatic hydrocarbons (PAH), PHC, and alkanes (ALK) were very good, good and fair, and in contrast, the predictions of the bioavailable concentrations of the PAH and PHC were only fair, and poor for ALK. A large number of trace elements, mainly lead (Pb), aluminium (Al), nickel (Ni), chromium (Cr), cadmium (Cd), iron (Fe) and zinc (Zn) were predicted with very good or good accuracy. The prediction results of the total HMs were also better than those of the bioavailable concentrations. Overall, the results demonstrate that VIS-NIR DRS coupled to RF is a promising rapid measurement tool to inform both the distribution and bioavailability of complex chemical mixtures without the need of collecting soil samples and lengthy extraction for further analysis.

Laboratory Visible and Near-Infrared Spectroscopy with Genetic Algorithm-Based Partial Least Squares Regression for Assessing the Soil Phosphorus Content of Upland and Lowland Rice Fields in Madagascar

As a laboratory proximal sensing technique, the capability of visible and near-infrared (Vis-NIR) diffused reflectance spectroscopy with partial least squares (PLS) regression to determine soil properties has previously been demonstrated. However, the evaluation of the soil phosphorus (P) content—a major nutrient constraint for crop production in the tropics—is still a challenging task. PLS regression with waveband selection can improve the predictive ability of a calibration model, and a genetic algorithm (GA) has been widely applied as a suitable method for selecting wavebands in laboratory calibrations. To develop a laboratory-based proximal sensing method, this study investigated the potential to use GA-PLS regression analyses to estimate oxalate-extractable P in upland and lowland soils from laboratory Vis-NIR reflectance data. In terms of predictive ability, GA-PLS regression was compared with iterative stepwise elimination PLS (ISE-PLS) regression and standard full-spectrum PLS (FS-PLS) regression using soil samples collected in 2015 and 2016 from the surface of upland and lowland rice fields in Madagascar (n = 103). Overall, the GA-PLS model using first derivative reflectance (FDR) had the best predictive accuracy (R2 = 0.796) with a good prediction ability (residual predictive deviation (RPD) = 2.211). Selected wavebands in the GA-PLS model did not perfectly match wavelengths of previously known absorption features of soil nutrients, but in most cases, the selected wavebands were within 20 nm of previously known wavelength regions. Bootstrap procedures (N = 10,000 times) using selected wavebands also confirmed the improvements in accuracy and robustness of the GA-PLS model compared to those of the ISE-PLS and FS-PLS models. These results suggest that soil oxalate-extractable P can be predicted from Vis-NIR spectroscopy and that GA-PLS regression has the advantage of tuning optimum bands for PLS regression, contributing to a better predictive ability.

Application of fractional-order derivative in the quantitative estimation of soil organic matter content through visible and near-infrared spectroscopy

The spectral preprocessing method has become an integral component of soil analysis through visible and near-infrared (Vis-NIR) spectroscopy. Various spectral pretreatment techniques are applied to improve model accuracy by removing undesired side effects, such as spectral noise, baseline shift, and light scattering, and by accentuating spectral features. Conventional integer-order derivatives (i.e., first and second derivatives), which represent specific cases of fractional-order derivatives (FODs), may neglect some detailed spectral information related to target variables. The objective of this study was to compare the performance of FOD in the estimation of soil organic matter (SOM) with that of conventional first and second derivatives. A total of 258 soil samples (180 for calibration and 78 for validation) were collected from Jianghan Plain, Central China. The reflectance spectra and SOM concentrations of the samples were obtained in the laboratory. Two regression techniques, namely, partial least squares (PLS) and PLS–support vector machine (PLS–SVM), and eight FOD transformation processes for spectral data were combined and compared. Results indicated that as the derivative order increased, the details of the FOD spectra changed and the spectral resolution of reflectance curves improved; the intensity of the spectral signals, however, weakened. The correlation between SOM and FOD spectra was enhanced at some specific wavelengths (e.g., the absolute value of the best one-dimensional correlation coefficient of the 1.25-order derivative spectra could reach 0.65 but that of the original reflectance spectra was only 0.47). In most cases, the PLS–SVM models performed better in SOM estimation than the PLS models. The PLS–SVM model with the 1.25-order derivative spectra exhibited the best model performance and provided the validation R2 and ratio of performance to an interquartile range of 0.79 and 3.03, respectively. FOD offers greater advantages in balancing the spectral resolution and the magnitude of spectral strength than traditional integer-order derivatives. Furthermore, the FOD algorithm has strong application potential in soil Vis-NIR spectroscopy and other types of spectroscopy.

The Optimal Local Model Selection for Robust and Fast Evaluation of Soluble Solid Content in Melon with Thick Peel and Large Size by Vis-NIR Spectroscopy

Soluble solid content (SSC) is one of the most important factors determining the quality and price of fresh fruits. However, the accurate assessment of SSC in some kinds of fruits with thick peel and large size is relatively difficult because it is important to select a suitable measurement position on this type of fruit. In this study, ‘Hami’ melon was used as the object of study, the visible and near-infrared (Vis-NIR) spectroscopy with spectral range of 550–950 nm was acquired from three positions (calyx, equator, and stem) of each sample to observe the effect of measurement positions on SSC assessment of whole ‘Hami’ melon. Three local models (calyx-region, equator-region, and stem-region models) and one global model based on the partial least squares (PLS) were developed with different preprocessing methods. Comparing all the established models, the results showed that the equator-region model and global model had the similar predictive performance which was better than ones of calyx-region and stem-region models. For improving the performance of models, the equator-region model and global model were further optimized based on different variable selection algorithms, including competitive adaptive reweighted sampling (CARS), uninformative variable elimination (UVE), combination algorithm CARS-SPA (successive projections algorithm), and combination algorithm UVE-SPA, respectively. And also, the linear multispectral PLS models and the nonlinear multispectral least squares support vector machine (LSSVM) models were established and compared using those selected characteristic variables, respectively. The results indicated that the performance of equator-region multispectral models was slightly superior to those of global multispectral models, and the optimal equator-region multispectral models were UVE-SPA-PLS (RP = 0.9143 and RMSEP = 0.8359) and CARS-SPA-LSSVM (RP = 0.9134 and RMSEP = 0.8958). The overall results indicated that it was feasible to develop the models using only the equator position information for detecting the SSC of whole ‘Hami’ melon. This study can provide some valuable references for building a fast and robust multispectral prediction model for SSC assessment in some kinds of fruits with thick peel and large size such as watermelon.

A geostatistical Vis-NIR spectroscopy index to assess the incipient soil salinization in the Neretva River valley, Croatia

Soil spectroscopy can provide low-cost and high-density data for predicting various soil properties. However, a relatively weak correlation between the spectra and the measurements of salinized soil makes spectroscopy difficult to use in predicting areas at risk of salinization, especially for low and moderately saline soils. The main objective of the study was to propose an effective approach based on Vis-NIR spectroscopy and geostatistics for mapping soil salinity in the Neretva River valley (Croatia). A spectral index (SI), which synthesizes most of salt affected soil properties, was defined and used as covariate in ordinary cokriging (COK) for improving electrical conductivity (ECe) prediction. The proposed approach was compared with a univariate predictor (ordinary kriging, OK), which uses only ECe data and a multivariate predictor (ordinary cokriging, COK) using more covariates such as some chemical properties of primary importance in salt affected soils (Ca2+, Mg2+, Na+, SO42−, Cl− concentrations and pH). The study was carried out in an agricultural area (5068 ha) located in the Neretva River valley (Croatia). Topsoil (0–30 cm) samples were collected at 246 locations with a grid (500 m × 500 m) sampling scheme and analyzed for some chemical and physical properties. Moreover, soil samples were used for visible and near infrared (Vis-NIR) spectra measurements with a range of wavelength between 350 and 2500 nm. The spectral data were pre-processed and ECe was predicted with partial least squares regression (PLSR). The first significant latent variable, which accounted for 85% of the total variance, was selected and used as a SI to quantify and map spatial variation of soil salinity. The univariate and multivariate geostatistical approaches provided results and performances quite similar. Regarding the two multivariate approaches, the one using only the spectral index as covariate has provided better results in terms of unbiasedness and accuracy. Moreover, the spectral index SI is also very cost-effective and it could then be used in possible broad-scale surveys for preventing soil salinity at landscape scale.

Simultaneous determination of six main types of lipid-soluble pigments in green tea by visible and near-infrared spectroscopy

Lipid-soluble pigments make great contributions to the color of green tea. This study aimed to rapidly and simultaneously measure six main types of lipid-soluble pigments in green tea by using the visible and near-infrared (Vis-NIR) spectroscopy. A total of 135 tea samples with five kinds and three grades were collected for spectral scanning and color measurement, and their lipid-soluble pigments contents were measured by high performance liquid chromatography. It can be found that tea color was closely related to the six pigments. And there were significant differences in lipid-soluble pigments contents among these kinds and grades. Finally, quantitative determination models of the six pigments obtained excellent results with Rp2 of 0.975, 0.973, 0.993, 0.919, 0.962 and 0.965 respectively based on multiple linear regression with the characteristic wavelengths. These results demonstrated that the Vis-NIR spectroscopy combined with chemometrics is a powerful tool for rapid determination of lipid-soluble pigments in green tea.

Application of Artificial Fish Swarm Algorithm for Synchronous Selection of Wavelengths and Spectral Pretreatment Methods in Spectrometric Analysis of Beef Adulteration

In this paper, we proposed artificial fish swarm algorithm (AF) for synchronous selection of wavelengths and pretreatment methods during quantification of beef adulteration with spoiled beef based on the analysis of ultraviolet (UV), visible and near-infrared (Vis-NIR), and UV-Vis-NIR spectra. The best partial least squares regression model was obtained when the wavelength-then-pretreatment scheme was adopted in the Vis-NIR range where 11 wavelengths were utilized to produce an RPD value of 2.68. The coefficients of determination R2 and root mean squared errors of the model were 0.91 and 0.08 as well as 0.87 and 0.11 for calibration and prediction, respectively. It was demonstrated that AF was a useful tool for model optimization as compared with genetic algorithm. Moreover, the sequence for selecting wavelengths and spectral pretreatment methods had great influence in model performance and could be decided by try-out approach since the performance due to selection sequence was sensitive to spectral range and optimization algorithm employed. Nevertheless, wavelength-then-pretreatment scheme was preferred due to simpler model structure and reduced computation loads. It was shown that Vis-NIR spectroscopy was feasible in quantifying beef adulteration, and AF was an advance method for optimizing model performance. Moreover, AF could be expended in more studies for optimizing food quality and safety analysis.

ASD — Highland Precision Agriculture case study: Improving precision agriculture with visible and near-infrared spectroscopy

Precision agriculture is an innovative and integrated approach to agriculture that aims to improve the efficiency of resource use and the decision-making surrounding variation on farms. During the past few decades, investments in precision agriculture research and technology have significantly increased worldwide. One such technological advance, near infrared spectroscopy, has been used as an analytical tool in the agriculture industry since the 1990s, and as instruments continue to evolve over time, the use of near infrared continues to progress. In this paper, current developments and future applications of visible and near-infrared spectroscopy (Vis-NIR) are discussed. The research aims to determine whether different pest infestation levels (in this case, the two-spotted spider mite) and other plant stressors on strawberry leaves can be distinguished spectrally using high-resolution Vis-NIR. Since research is still underway, the development of commercially viable monitoring methods remains in its infancy. However, the goal is to advance remote sensing techniques and image analytics to the point where fields can be scanned more quickly, efficiently, and accurately than with traditional monitoring methods.

A systematic study on the application of scatter-corrective and spectral-derivative preprocessing for multivariate prediction of soil organic carbon by Vis-NIR spectra

Soil organic carbon (SOC) plays a crucial role as an ecosystems indicator. Its quantification requires an affordable, and less time-consuming method. Visible and near infrared (Vis-NIR) reflectance spectroscopy has demonstrated its applicability to predict SOC over the years. The aims of this study were to i) to compare the influence of preprocessing techniques on prediction performance, ii) assess the modeling performance of a wide range of multivariate methods, and iii) evaluate the potential of Vis-NIR spectroscopy to predict SOC. Soil sampling was conducted over an area of approximately 1800km2 in the Southern region of Brazil, where a total of 595 soil samples were collected. Oxisols are predominant in the area following by Entisols and Inceptisols.The seven preprocessing techniques that were employed can be divided into two categories based on their SOC prediction performance: scatter-correction and spectral-derivatives. A total of nine different methods were evaluated to predict SOC from Vis-NIR spectra. The models that use scatter-corrective preprocessing exhibited superior prediction compared to the spectral-derivatives group. In the scatter-correction group, continuum removal was the most suitable preprocessing method for SOC prediction. Except for random forest (RF), all the multivariate methods presented robust predictions. The best fit and highest model accuracy for SOC models in validation mode were achieved when applying the weighted average partial least-squares (WAPLS) method and normalization by range (NBR) preprocessing (R2=0.82, root mean square error=0.48%, and ratio of the performance to the interquartile range=3.18). Findings from this systematic methodology study identified the reliability of SOC determinations by examining how preprocessing techniques and multivariate methods affect spectral analyses. It also guides future studies to select the most appropriate methods on similar soils.

Prediction and Mapping of Soil Attributes using Diffuse Reflectance Spectroscopy and Magnetic Susceptibility

Core Ideas We used VIS‐NIR spectroscopy and magnetic susceptibility to predict soil attributes. This paper shows the importance these tools for mapping large areas with detail scale. Indirect measurements are useful to characterize spatial variability of attributes. The maps predicted showed the same spatial pattern that the observed maps. The methodologies used are much simpler and faster than conventional methods. The development of fast, accurate and low‐cost methods to quantify soil attributes is of paramount importance to enable detailed mapping, mainly in tropical regions where there is great variation of the chemical, physical and mineralogical attributes. Therefore, the aims of this paper were (i) to investigate if visible and near infrared (VIS‐NIR) spectroscopy and magnetic susceptibility (MS) can be applied to determine soil attributes at the sandstone‐basaltic transition and (ii) evaluate and map their spatial distribution. Calibration models based on VIS‐NIR spectroscopy and MS were developed separately for each attribute. Soil samples (0–25 cm depth) were collected at 446 sites, air‐dried and passed through a 2‐mm sieve and analyzed in the laboratory. To develop models based on soil spectra and laboratory data, the partial least squares regression (PLSR) was used. Already, the MS‐based models were calibrated by linear regression between magnetic and laboratory data. The best prediction accuracy parameters were obtained with MS, later with VIS‐NIR and lastly with VIS. The more accurate results between the observed and predicted values were found for iron oxide extracted by dithionite (R2 = 0.89, RRMSE = 0.02), clay (R2 = 0.85, RRMSE = 0.76) and total carbon (R2 = 0.83, RRMSE = 1.18) estimated by MS, revealing that this is a good predictor of key properties of studied soils, even with wide chemical and mineralogical variation. Both tools are very attractive for the strategic planning of land use and occupation, mapping large areas with detailed scale, environmental monitoring and precision agriculture.

Vis-NIR Spectroscopy and PLS Regression with Waveband Selection for Estimating the Total C and N of Paddy Soils in Madagascar

Visible and near-infrared (Vis-NIR) diffuse reflectance spectroscopy with partial least squares (PLS) regression is a quick, cost-effective, and promising technology for predicting soil properties. The advantage of PLS regression is that all available wavebands can be incorporated in the model, while earlier studies indicate that PLS models include redundant wavelengths, and selecting specific wavebands can refine PLS analyses. This study evaluated the performance of PLS regression with waveband selection using Vis-NIR reflectance spectra to estimate the total carbon (TC) and total nitrogen (TN) in soils collected mainly from the surface of upland and lowland rice fields in Madagascar (n = 59; after outliers were removed). We used iterative stepwise elimination-based PLS (ISE-PLS) to estimate soil TC and TN and compared the predictive ability with standard full-spectrum PLS (FS-PLS). The predictive abilities were assessed using the coefficient of determination (R2), the root mean squared error of cross-validation (RMSECV), and the residual predictive deviation (RPD). Overall, ISE-PLS using first derivative reflectance (FDR) showed a better predictive accuracy than ISE-PLS for both TC (R2 = 0.972, RMSECV = 0.194, RPD = 5.995) and TN (R2 = 0.949, RMSECV = 0.019, RPD = 4.416) in the soil of Madagascar. The important wavebands for estimating TC (12.59% of all wavebands) and TN (3.55% of all wavebands) were selected from all 2001 wavebands over the 400–2400 nm range using ISE-PLS. These findings suggest that ISE-PLS based on Vis-NIR diffuse reflectance spectra can be used to estimate soil TC and TN contents in Madagascar with an improved predictive accuracy.

Visible and near infrared spectroscopy coupled to random forest to quantify some soil quality parameters

This study evaluates the use of visible and near infrared spectroscopy (Vis-NIRS) combined with multivariate regression based on random forest to quantify some quality soil parameters. The parameters analyzed were soil cation exchange capacity (CEC), sum of exchange bases (SB), organic matter (OM), clay and sand present in the soils of several regions of Brazil. Current methods for evaluating these parameters are laborious, timely and require various wet analytical methods that are not adequate for use in precision agriculture, where faster and automatic responses are required. The random forest regression models were statistically better than PLS regression models for CEC, OM, clay and sand, demonstrating resistance to overfitting, attenuating the effect of outlier samples and indicating the most important variables for the model. The methodology demonstrates the potential of the Vis-NIR as an alternative for determination of CEC, SB, OM, sand and clay, making possible to develop a fast and automatic analytical procedure.

Using laboratory Vis-NIR spectroscopy for monitoring some forest soil properties

The development of rapid, accurate, and cost-effective methods to determine forest soil properties such as visible and near infrared (Vis-NIR) spectroscopy is important for sustainable land management. The main objective of this study was to assess the suitability of Vis-NIR spectroscopy coupled with partial least squares regression (PLSR) to determine some forest soil properties such as organic carbon (SOC), total nitrogen (TN), pH, and soil texture (sand, silt, and clay) for a representative forest area in southern Italy. Soil samples (0–20 cm depth) were collected at 267 locations, oven-dried and passed through a 2-mm sieve, and analyzed for some chemical and physical soil properties using conventional laboratory methods. Vis-NIR reflectance of each soil sample was measured in laboratory under artificial light using an ASD FieldSpec IV 350–2500 nm spectroradiometer. Partial least squares regression (PLSR) was used to develop a calibration model for SOC, TN, pH, sand, silt, and clay. Samples were split into a calibration set (187 samples) to develop the models and a validation set (80 samples) to assess the prediction accuracy of the calibration models. Results showed a good agreement between measured and predicted values with high R 2 and low root mean square error (RMSE) values. Model validation using independent data was satisfactory for all the studied soil properties. Finally, findings confirmed that laboratory Vis–NIR spectroscopy has the potential to be a non-destructive and cost-effective tool for rapid determination of many soil properties. The spectral data collected in this study could contribute to build a regional soil spectral library to be used advantageously in support to soil survey in other areas of the Calabria region (southern Italy).

Spectroscopic-based assessment of the content and geochemical behaviour of arsenic in a highly heterogeneous sulphide-rich mine waste dump

Soil pollution by arsenic is a serious environmental problem in many mining areas. Quick identification of the amount and extent of the pollution is an important basis for developing appropriate remediation strategies. In a case study, 55 soil samples were collected from a highly heterogeneous waste dump around the Sarcheshmeh copper mine, south east Iran. Samples’ visible and near-infrared (VNIR) reflectance spectra were measured, transformed to absorbance and then pre-processed using Savitzky–Golay first-derivative (FD) and Savitzky–Golay second-derivative (SD) transformation methods. The obtained spectra were then subjected to three regression models including principal component regression (PCR), partial least squares regression (PLSR) and support vector regression (SVR) for predicting arsenic concentration. The best prediction accuracies were obtained by SVR and PLSR methods applied on first-derivative pre-processed spectra with R 2 values of 0.81 and 0.69, respectively. It was found that VNIR spectroscopy is a successful method for predicting As concentration in contaminated soils of the dumpsites. Study of the prediction mechanism showed that the intercorrelation between arsenic and spectral features of soil including iron oxy/hydroxides and clay minerals was the major mechanism enabling the prediction of arsenic concentration. However, higher values of correlation coefficients at ~460, ~560 and ~590 nm suggested the internal association between arsenic and iron minerals as the more important mechanism for prediction. This conclusion supported previous speciation studies conducted in the same waste dump using improved correlation analysis and chemical sequential extraction method.

Sensor data fusion for soil health assessment

Assessment of soil health involves determining how well a soil is performing its biological, chemical, and physical functions relative to its inherent potential. Due to high cost, labor requirements, and soil disturbance, traditional laboratory analyses cannot provide high resolution soil health data. Therefore, sensor-based approaches are important to facilitate cost-effective, site-specific management for soil health. In the Central Claypan Region of Missouri, USA, visible and near-infrared (VNIR) diffuse reflectance spectroscopy has successfully been used to estimate biological components of soil health as well as Soil Management Assessment Framework (SMAF) scores. In contrast, estimation models for important chemical and physical aspects of soil health have been less successful with VNIR spectroscopy. The primary objective of this study was to apply a sensor fusion approach to estimate soil health indicators and SMAF scores using VNIR spectroscopy in conjunction with soil apparent electrical conductivity (ECa), and penetration resistance measured by cone penetrometer (i.e., cone index, CI). Soil samples were collected from two depths (0–5 and 5–15cm) at 108 locations within a 10-ha research site encompassing different cropping systems and landscape positions. Soil health measurements and VNIR spectral data were obtained in the laboratory, while CI and ECa data were obtained in situ. Calibration models were developed with partial least squares (PLS) regression and model performance was evaluated using coefficient of determination (R2) and root mean square error (RMSE). Models integrating ECa and CI with VNIR reflectance data improved estimates of the overall SMAF score (R2=0.78, RMSE=7.21%) relative to VNIR alone (R2=0.69, RMSE=8.41%), reducing RMSE by 14%. Improved models were also achieved for estimates of the individual chemical, biological, and physical soil health scores, demonstrating reductions in RMSE of 2.8, 5.4, and 10.0%, respectively. The results of this study illustrate the potential for rapid quantification of soil health by fusing VNIR sensor data with auxiliary data obtained from complementary sensors.

Monitoring soil organic carbon content using Vis-NIR spectroscopy: A case study in southern Italy

Assessment and monitoring of soil organic carbon (SOC) quality are important for understanding SOC dynamics and developing management practices of soils. This study investigated the feasibility of visible and near infrared (Vis-NIR) spectroscopy as a fast method to determining and monitoring the SOC content in a mountains area of the Calabria region (southern Italy). In this study, a multitemporal data set including 299 topsoil samples was used: 208 soil samples were collected in 2012 whereas 91 in 2015. The soil samples were collected using a metallic core cylinder with a diameter of 7.5 cm and a height of 20 cm. The Vis-NIR reflectance of soil samples was measured in laboratory, under artificial light, using an ASD FieldSpec IV 350-2500 nm spectroradiometer (Analytical Spectral Devices Inc., Boulder, Colorado, USA), while SOC was determined using a Shimadzu TOC-analyzer with a SSM-5000A solid sample module (Shimadzu Corporation, Kyoto, Japan). In order, to develop a prediction model based on soil spectra and reference laboratory data of SOC the partial least squared regression (PLSR) analysis was applied. Therefore, prior to develop calibration model spectra pre-treatment was carried out to minimize noise and optimize calibration accuracy from the data. PLSR prediction model was developed using the soil samples collected in the 2012. The optimum number of factors to retain in the PLSR models were determined by cross validation. The model was tested using the soil data sampled in the 2015. Results revealed a high level of agreement between measured and predicted values with high coefficients of determination (R2) and low root mean square error (RMSE). These results indicate that Vis-NIR spectroscopy could a promising method for determine and monitoring the SOC content.

Vis-NIR spectroscopy with moving-window PLS method applied to rapid analysis of whole blood viscosity.

A rapid analytical method of human whole blood viscosity with low, medium, and high shear rates [WBV(L), WBV(M), and WBV(H), respectively] was developed using visible and near-infrared (Vis-NIR) spectroscopy combined with a moving-window partial least squares (MW-PLS) method. Two groups of peripheral blood samples were collected for modeling and validation. Separate analytical models were established for male and female groups to avoid interference in different gender groups and improve the homogeneity and prediction accuracy. Modeling was performed for multiple divisions of calibration and prediction sets to avoid over-fitting and achieve parameter stability. The joint analysis models for three indicators were selected through comprehensive evaluation of MW-PLS. The selected joint analysis models were 812-1278nm for males and 670-1146nm for females. The root-mean-square errors (SEP) and the correlation coefficients of prediction (RP) for all validation samples were 0.54mPa•s and 0.91 for WBV(L), 0.25mPa•s and 0.92 for WBV(M), and 0.22mPa•s and 0.90 for WBV(H). Results indicated high prediction accuracy, with prediction values similar to the clinically measured values. Overall, the findings confirmed the feasibility of whole blood viscosity quantification based on Vis-NIR spectroscopy with MW-PLS. The proposed rapid and simple technique is a promising tool for surveillance, control, and treatment of cardio-cerebrovascular diseases in large populations. Graphical Abstract The caption/legend of the online abstract figure: The selected wavebands and the prediction effects for the three indicators of whole blood viscosity.

Improving Spectral Estimation of Soil Organic Carbon Content through Semi-Supervised Regression

Visible and near infrared (VIS-NIR) spectroscopy has been applied to estimate soil organic carbon (SOC) content with many modeling strategies and techniques, in which a crucial and challenging problem is to obtain accurate estimations using a limited number of samples with reference values (labeled samples). To solve such a challenging problem, this study, with Honghu City (Hubei Province, China) as a study area, aimed to apply semi-supervised regression (SSR) to estimate SOC contents from VIS-NIR spectroscopy. A total of 252 soil samples were collected in four field campaigns for laboratory-based SOC content determinations and spectral measurements. Semi-supervised regression with co-training based on least squares support vector machine regression (Co-LSSVMR) was applied for spectral estimations of SOC contents, and it was further compared with LSSVMR. Results showed that Co-LSSVMR could improve the estimations of SOC contents by exploiting samples without reference values (unlabeled samples) when the number of labeled samples was not excessively small and produce better estimations than LSSVMR. Therefore, SSR could reduce the number of labeled samples required in calibration given an accuracy threshold, and it holds advantages in SOC estimations from VIS-NIR spectroscopy with a limited number of labeled samples. Considering the increasing popularity of airborne platforms and sensors, SSR might be a promising modeling technique for SOC estimations from remotely sensed hyperspectral images.

Quantitative Estimation of Organic Matter Content in Arid Soil Using Vis-NIR Spectroscopy Preprocessed by Fractional Derivative

Soil organic matter (SOM) content is an important index to measure the level of soil function and soil quality. However, conventional studies on estimation of SOM content concerned about the classic integer derivative of spectral data, while the fractional derivative information was ignored. In this research, a total of 103 soil samples were collected in the Ebinur Lake basin, Xinjiang Uighur Autonomous Region, China. After measuring the Vis-NIR (visible and near-infrared) spectroscopy and SOM content indoor, the raw reflectance and absorbance were treated by fractional derivative from 0 to 2nd order (order interval 0.2). Partial least squares regression (PLSR) was applied for model calibration, and five commonly used precision indices were used to assess the performance of these 22 models. The results showed that with the rise of order, these parameters showed the increasing or decreasing trends with vibration and reached the optimal values at the fractional order. A most robust model was calibrated based on 1.8 order derivative of R, with the lowest RMSEC (3.35 g kg−1) and RMSEP (2.70 g kg−1) and highest Rc2 (0.92), Rp2 (0.91), and RPD (3.42 > 3.0). This model had excellent predictive performance of estimating SOM content in the study area.