Visible Near-infrared Reflectance Spectroscopy Research Articles

Development of predictive models for quality and maturation stage attributes of wine grapes using vis-nir reflectance spectroscopy

The viticulture business has an increasing demand for high quality products directly impacting on their market acceptance. Hence, the constant monitoring activities of vineyards, specially quality and maturation stage attributes, are important to assure the production of special wines, justified by their high value-added characteristic. Therefore, optical technologies appear as promising techniques for non-destructive analysis of wine grapes in view of reducing agricultural inputs and analysis duration. The main objective of the present work was to develop predictive models for quality and maturation stage attributes of wine grapes using visible/near infrared (VIS-NIR) reflectance spectroscopy. A total of 432 ‘Syrah’ and 576 ‘Cabernet Sauvignon’ berries were collected and their reflectance spectra were acquired using FieldSpec® 3 spectroradiometer for a spectral range from 450 to 1800 nm. In a posterior step, total soluble solids, total anthocyanins and yellow flavonoids were determined as the reference standards. Before elaborating mathematical models, the spectral data were submitted to several pretreatments, such as smoothing, derivate and corrections. Principal Component Regression (PCR) and Partial Least Squares Regression (PLSR) were utilized as predictive models using both the complete spectrum data (450–1800 nm) and a smaller set of spectral samples selected by the Jack-Knife method. Other predictive models were also developed utilizing Multiple Linear Regression (MLR) with spectral signatures from quality attributes. Maturation stages were discriminated using Principal Component Analysis – Linear Discriminant Analysis (PCA-LDA), Principal Component Analysis – Quadratic Discriminant Analysis (PCA-QDA), Principal Component Analysis – Linear Discriminant Analysis using Mahalanobis distance (PCA-LDA Mahalanobis), and Partial Least Squares Discriminant Analysis (PLS-DA) classification techniques. The construction of the PCR, PLSR and MLR regression models has provided robust predictions for total soluble solids and anthocyanins contents (R2 ≥ 0.90), as well as flavonoids contents with a certain degree of precision (R2 ≥ 0.70). Moreover, it was possible to differentiate distinct maturation stages of vines with 93.15% of accuracy using PLS-DA. Therefore, VIS-NIR reflectance spectroscopy is a powerful tool for non-destructive evaluation of quality and maturation stage attributes of intact grapes from ‘Syrah’ and ‘Cabernet Sauvignon’ grapes.

A Spectral Comparison of Jarosites Using Techniques Relevant to the Robotic Exploration of Biosignatures on Mars.

The acidic sulfate-rich waters of the Meridiani Planum region were potentially a habitable environment for iron-oxidizing bacteria on ancient Mars. If life existed in this ancient martian environment, jarosite minerals precipitating in these waters may record evidence of this biological activity. Since the Meridiani jarosite is thermodynamically stable at the martian surface, any biosignatures preserved in the jarosites may be readily available for analysis in the current surface sediments during the ongoing robotic exploration of Mars. However, thermal decomposition experiments indicate that organic compound detection of sediments containing jarosite may be challenging when using pyrolysis experiments; the instrument commonly used to assess organic matter in martian samples. So, here, we assess if the biogenicity of the Meridiani-type jarosites can be determined using complimentary spectroscopic techniques also utilized during the robotic exploration of Mars, including the upcoming ExoMars2020 rover mission. An abiotic jarosite, synthesized following established protocols, and a biological jarosite counterpart, derived from a microbial enrichment culture of Rio Tinto river sediments, were used to compare four spectroscopy techniques employed in the robotic exploration of Mars (Raman spectroscopy, mid-infrared (IR) spectroscopy, visible near-infrared reflectance (VNIR) spectroscopy and Mössbauer spectroscopy) to determine if the complimentary information obtained using these instruments can help elucidate the biological influence of Meridiani-type jarosites. Raman spectral differences might be due to the presence of unreacted reagents in the synthetic spectra and not biological contributions. Reflectance (IR/VNIR) spectra might exhibit minor organic absorption contributions, but are observed in both sample spectra, and do not represent a biosignature. Mössbauer spectra show minor differences in fit parameters that are related to crystal morphology and are unrelated to the biological (i.e., organic) component of the system. Results of this study suggest that the identification of biosignatures in Meridiani-type jarosites using the in situ robotic exploration on Mars may be possible but will be challenging. Our work provides additional insight into extraterrestrial biosignature detection and data interpretation for Mars exploration and indicates that sample return missions are likely required to unequivocally resolve the possible biogenicity of the Meridiani sediments or other jarosite-containing sediments.

Prediction of soil organic carbon with different parent materials development using visible-near infrared spectroscopy

The storage of soil organic carbon (SOC) should improve soil fertility. Conventional determination of SOC is expensive and tedious. Visible-near infrared reflectance spectroscopy is a practical and cost-effective approach that has been successfully used SOC concentration. Soil spectral inversion model could quickly and efficiently determine SOC content. This paper presents a study dealing with SOC estimation through the combination of soil spectroscopy and stepwise multiple linear regression (SMLR), partial least squares regression (PLSR), principal component regression (PCR). Spectral measurements for 106 soil samples were acquired using an ASD FieldSpec 4 standard-res spectroradiometer (350–2500 nm). Six types of transformations and three regression methods were applied to build for the quantification of different parent materials development soil. The results show that (1)the basaltic volcanic clastics development of SOC spectral response bands located in 500 nm, 800 nm; Trachyte spectral response of the soil quality, and the volcanic clastics development at 405 nm, 465 nm, 575 nm, 1105 nm. (2) Basaltic volcanic debris soil development, first deviation of maximum correlation coefficient is 0.8898; thick surface soil of the development of rocky volcanic debris from bottom reflectivity logarithm of first deviation of maximum correlation coefficient is 0.9029. (3) Soil organic matter content of basaltic volcanic clastics development optimal prediction model based on spectral reflectance inverse logarithms of first deviation of SMLR. Independent variable number is 7, Rv2 = 0.9720, RMSEP = 2.0590, sig = 0.003. Trachyte qualitative volcanic clastics developed soil organic matter content of the optimal prediction model based on spectral reflectance inverse logarithms of first deviation of PLSR. Model number of the independent variables Pc = 5, Rc = 0.9872, Rc2 = 0.9745, RMSEC = 0.4821, SEC = 0.4906, forecasts determine coefficient Rv2 = 0.9702, RMSEP = 0.9563, SEP = 0.9711, Bias = 0.0637.

Prediction Accuracy of Soil Organic Carbon from Ground Based Visible Near-Infrared Reflectance Spectroscopy

The present study was conducted to predict soil organic carbon (SOC) from ground visible near-infrared (Vis–NIR, 400–2500 nm) spectroradiometer reflectance spectra. The objective was to study the effect of various pre-processing methods and prediction models on the accuracy of SOC estimated. Measured SOC content and reflectance spectra from pasture and cotton fields of Narrabri, Australia were used in the analysis. Reflectance spectra were pretreated with different smoothing methods such as: moving average, median filtering, gaussian smoothing and Savitzky Golay smoothing. A comparison between principal component regression, partial least square regression (PLSR) and artificial neural network models was carried out to get an optimum model for organic carbon prediction. The results indicate that PLSR model performs better with Savitzky Golay as the best pre-processing method for the study area, yielding $$ R_{\text{cal}}^{2} = \, 0.84 $$ , RPDcal = 2.55 and RPIQcal = 4.02 in the calibration set and $$ R_{\text{val}}^{2} = \, 0.77 $$ , RPDval = 2.17 and RPIQval = 3.19 in the validation set. The study recommends a suitable method in case of limited number of soil data. Based on the study, it can be said that properly pre-treated reflectance spectra show tremendous potential in soil organic carbon prediction.

Prediction of total carbon, total nitrogen and pH of organic materials using visible near infrared reflectance spectroscopy

We used visible near-infrared reflectance spectroscopy (VNIRS) to characterize a set of organic materials (n = 75). Good predictions for total carbon, excellent predictions for total nitrogen, but ...

Different shades of red: The complexity of mineralogical and physico-chemical factors influencing the colour of ceramics

Different techniques (X-ray diffraction, field emission scanning electron microscope, colorimetry, visible-near infrared reflectance spectroscopy) were carried out to investigate the cause of colour changes of traditional ceramic materials. Two clayey materials of different composition, collected in the Bay of Naples, were fired in oxidising atmosphere at different temperatures resulting in different shades of red colour.Hematite is responsible of the reddish hue of ceramics and its nucleation is strictly related to firing temperature and chemical composition of the raw materials. A low CaO concentration allowed hematite to form in higher amounts providing a more intense reddish hue at high firing temperatures (over 950°C). At the highest temperature (1100°C) all samples showed darker colour due to increased size of iron oxide particles. Black core developed in Ca-rich ceramics fired at low temperatures as the short time of firing is insufficient to complete iron oxidation within the matrix, except in those containing high temper amounts. Indeed, microstructural modification occurs due to the presence of discontinuities among temper grains and matrix, which improves the circulation of oxygen in the core of ceramics.

Comparing predictive ability of laser-induced breakdown spectroscopy to visible near-infrared spectroscopy for soil property determination

Soil organic carbon (SOC) and particle size fractions have a practical value for agronomy and the environment. Thus, alternative techniques to replace the expensive conventional analyses of soil are needed. Visible near-infrared reflectance spectroscopy (vis–NIRS) has already shown potential for becoming an alternative method for soil analysis since it is faster and cheaper than conventional methods. Laser-induced breakdown spectroscopy (LIBS) is another cost-effective technique with potential for rapid analysis of elements present in the soil. In this study, the feasibility of using LIBS to determine SOC, clay, silt and sand contents of Danish agricultural soils was tested and compared with the vis–NIRS method. First, country-scale Partial Least Squares (PLS) regression models on soils collected across Denmark (N = 78) were built and validated using independent field samples (N = 54). Secondly, the country-scale calibration data set was spiked with 14 representative samples from the fields and validated with the 54 field samples. Generated country-scale LIBS models exhibited similar and not significantly different (p > 0.05) results to vis–NIRS for all soil properties except a significantly higher (p = 0.0305) predictive ability for sand. Spiking improved the accuracy of most of the LIBS and vis–NIRS models, indicating the importance of similarities between the calibration and the validation data sets. No significant differences (p > 0.05) were found between the LIBS and vis–NIRS spiked country-scale models. Lower prediction errors for most properties were obtained using LIBS, rendering it an equally good or even a more accurate technique for soil properties determination than the well-established vis–NIRS method.

Visible near infrared reflectance spectroscopy prediction of soil heavy metal concentrations in paper mill biosolid- and liming by-product-amended agricultural soils

Continuous assessment of soil heavy metal concentrations in agricultural soils is critical for ensuring ecosystem health and minimizing potential adverse effects. However, conventional laboratory determinations are time-consuming, expensive and involve hazardous chemicals. We evaluated the potential of visible near infrared reflectance spectroscopy (VNIRS) to rapidly and simultaneously predict total, Mehlich-3 (M3), DTPA, and water-soluble (H2O) soil heavy metal concentrations. Soil samples were collected from a loam soil (n=64) and a loamy sand (n=32) after repeated annual applications (3–9years) of paper mill biosolids (PB) and liming by-products. Models were developed using modified partial least squares regression on 75% of the total sample set and also for each soil, after removal of the spectral outliers (n=3), with the remainder used for validation. The coefficient of determination in validation (R2V) and the ratio of performance to deviation (RPD) were used to assess model accuracy. Predictions for the total set were excellent for total Zn and Cd, M3-Cu and -Cd, and DTPA-Cd (R2V>0.90, RPD>3.0), good for total Cu, Ni and Mo, M3-Ni, DTPA-Cu and -Ni, and H2O-Cu and -Ni (R2V=0.70 to 0.90, RPD=1.70 to 3.0), but poor for M3-Zn, DTPA-Zn, and H2O-Zn, -Cd and -Mo (R2V<0.70, RPD<1.70). Except for one case, site-specific models were either more accurate or provided similar results with the model of the total set in predicting site-specific concentrations. The relationship with soil organic matter (SOM) was the major predictive mechanism for Cd in our dataset, while the prediction of other soil heavy metals was due to the combined influence of SOM and Fe oxides. We conclude that VNIRS has the potential to predict total, M3, DTPA and to a lesser extent water-soluble soil heavy metals, especially when developed using site-specific samples.

Visible-Near Infrared Reflectance Spectroscopy for Rapid Characterization of Salt-Affected Soil in the Indo-Gangetic Plains of Haryana, India

Management of salt-affected soils is a challenging task in the input intensive rice-wheat cropping zone of the Indo-Gangetic plains (IGP). Timely detection of salt-affected areas and assessment of the degree of severity are vital in order to narrow down the potential gap in yield. Conventional laboratory techniques of saturation extract electrical conductivity (ECe) and sodium adsorption ration (SAR) for soil salinity assessment are time-consuming and labour intensive; the VNIR (visible-near infrared) reflectance spectroscopy technique provides ample information on salinity and its attributes in an efficient and cost-effective way. This study aims to develop robust soil reflectance spectral models for rapid assessment of soil salinity in the salt affected areas of the IGP region of Haryana using VNIR reflectance spectroscopy. The results indicated that the spectral region between 1390 and 2400 nm was highly sensitive to measure changes in salinity. The developed hyperspectral models explained more than 80 % variability in ECe, and other salinity related attributes (saturated extract Na+, Ca2+ + Mg2+, Cl− and SAR) in the validation datasets. With the increasing availability of data from hyperspectral sensors in near future, the study will be very useful in real time monitoring of soils in the spatio-temporal context; enabling the farmers of IGP area to deal with salt degradation more effectively and efficiently.

Visible near infrared reflectance spectroscopy to predict soil phosphorus pools in chernozems of Saskatchewan, Canada

To date, there are few direct or rapid methods to quantify the concentration of many soil phosphorus (P) pools, including organic P (OP). Visible near infrared reflectance spectroscopy (VNIRS) is a rapid, inexpensive, and accurate technique for analyzing a wide variety of organic materials and is increasingly used in soil science. The aim of this study was to examine the potential of VNIRS to predict the concentrations of a number of soil P pools, including OP, total P analyzed by digestion (TP), available P using the Olsen (POls) and Mehlich-3 (PM3) methods, and other related soil chemical properties [organic matter (SOM) and Mehlich-3 extractable Al, Fe, Ca, Mg, and Mn] that could influence the soil P cycle. Soil samples (n=360) were taken from experimental sites near Indian Head, SK, Canada, from short-term (8yr) and long-term (31yr) no-tillage plots of a field pea (Pisum sativum L.)–spring wheat (Triticum aetivum L.) rotation receiving five P fertilizer application rates annually. We randomly selected 80% of each sample set for calibration, while the remaining 20% was used for validation. Prediction models were developed using modified partial least squares regression with cross-validation. The VNIRS models were evaluated using the coefficient of determination of validation (Rv2) and the ratio of standard error of prediction to standard deviation (RPD). The VNIRS predictions for OP were moderately successful for the total soil sample set and for the short-term set (0.80≤Rv2<0.90 and 2.25≤RPD<3.00), and were moderately useful for the long-term set (0.70≤Rv2<0.80 and 1.75≤RPD<2.25). The VNIRS predictions were successful for soil CaM3 and SOM, moderately successful for MgM3, moderately useful for POls, PM3, and AlM3, and less reliable for TP, FeM3, and MnM3. This study demonstrated that VNIRS is a promising analysis technique for soil OP, POls, PM3, CaM3, AlM3, MgM3, and SOM. Success in predictions of OP, POls, and PM3 may be attributed to their relationship to SOM and/or to CaM3 or MgM3.

Comparing different data preprocessing methods for monitoring soil heavy metals based on soil spectral features

The lands near mining industries in the Czech Republic are subjected to soil pollution with heavy metals. Excessive heavy metal concentrations in soils not only dramatically impact the soil quality, but also due to their persistent nature and indefinite biological half-lives, potentially toxic metals can accumulate in the food chain and can eventually endanger human health. Monitoring and spatial information of these elements require a large number of samples and cumbersome and time-consuming laboratory measurements. A faster method has been developed based on a multivariate calibration procedure using support vector machine regression (SVMR) with cross-validation, to establish a relationship between reflectance spectra in the visible-near infrared (Vis-NIR) region and concentration of Mn, Cu, Cd, Zn, and Pb in soil. Spectral preprocessing methods, first and second derivatives (FD and SD), standard normal variate (SNV), multiplicative scatter correction (MSC), and continuum removal (CR) were employed after smoothing with Savitzky-Golay to improve the robustness and performance of the calibration models. According to the criteria of maximal coefficient of determination (R2cv) and minimal root mean square error of prediction in cross-validation (RMSEPcv), the SVMR algorithm with FD preprocessing was determined as the best method for predicting Cu, Mn, Pb, and Zn concentration, whereas the SVMR model with CR preprocessing was chosen as the final method for predicting Cd. Overall, this study indicated that the Vis-NIR reflectance spectroscopy technique combined with a continuously enriched soil spectral library as well as a suitable preprocessing method could be a nondestructive alternative for monitoring of the soil environment. The future possibilities of multivariate calibration and preprocessing with real-time remote sensing data have to be explored.

Using Visible and Near-infrared Reflectance Spectroscopy to Estimate Total Nitrogen Contents for Different Soil Types in the Sanjiangyuan Regions

Using Visible and Near-infrared Reflectance Spectroscopy to Estimate Total Nitrogen Contents for Different Soil Types in the Sanjiangyuan Regions

Application of visible/near-infrared reflectance spectroscopy for predicting internal and external quality in pepper.

The characterization of internal (°Brix, pH, malic acid, total phenolic compounds, ascorbic acid and total carotenoid content) and external (color, firmness and pericarp wall thickness) pepper quality is necessary to better understand its possible applications and increase consumer awareness of its benefits. The main aim of this work was to examine the feasibility of using visible/near-infrared reflectance spectroscopy (VIS-NIRS) to predict quality parameters in different pepper types. Commercially available spectrophotometers were evaluated for this purpose: a Polychromix Phazir spectrometer for intact raw pepper, and a scanning monochromator for freeze-dried pepper. The RPD values (ratio of the standard deviation of the reference data to the standard error of prediction) obtained from the external validation exceeded a value of 3 for chlorophyll a and total carotenoid content; values ranging between 2.5 < RPD < 3 for total phenolic compounds; between 1.5 < RPD <2.5 for °Brix, pH, color parameters a* and h* and chlorophyll b; and RPD values below 1.5 for fruit firmness, pericarp wall thickness, color parameters C*, b* and L*, vitamin C and malic acid content. The present work has led to the development of multi-type calibrations for pepper quality parameters in intact and freeze-dried peppers. The majority of NIRS equations obtained were suitable for screening purposes in pepper breeding programs. Components such as pigments (xanthophyll, carotenes and chlorophyll), glucides, lipids, cellulose and water were used by modified partial least-squares regression for modeling the predicting equations. © 2015 Society of Chemical Industry.

Modeling Soil Organic Carbon at Regional Scale by Combining Multi-Spectral Images with Laboratory Spectra.

There is a great challenge in combining soil proximal spectra and remote sensing spectra to improve the accuracy of soil organic carbon (SOC) models. This is primarily because mixing of spectral data from different sources and technologies to improve soil models is still in its infancy. The first objective of this study was to integrate information of SOC derived from visible near-infrared reflectance (Vis-NIR) spectra in the laboratory with remote sensing (RS) images to improve predictions of topsoil SOC in the Skjern river catchment, Denmark. The second objective was to improve SOC prediction results by separately modeling uplands and wetlands. A total of 328 topsoil samples were collected and analyzed for SOC. Satellite Pour l’Observation de la Terre (SPOT5), Landsat Data Continuity Mission (Landsat 8) images, laboratory Vis-NIR and other ancillary environmental data including terrain parameters and soil maps were compiled to predict topsoil SOC using Cubist regression and Bayesian kriging. The results showed that the model developed from RS data, ancillary environmental data and laboratory spectral data yielded a lower root mean square error (RMSE) (2.8%) and higher R2 (0.59) than the model developed from only RS data and ancillary environmental data (RMSE: 3.6%, R2: 0.46). Plant-available water (PAW) was the most important predictor for all the models because of its close relationship with soil organic matter content. Moreover, vegetation indices, such as the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), were very important predictors in SOC spatial models. Furthermore, the ‘upland model’ was able to more accurately predict SOC compared with the ‘upland & wetland model’. However, the separately calibrated ‘upland and wetland model’ did not improve the prediction accuracy for wetland sites, since it was not possible to adequately discriminate the vegetation in the RS summer images. We conclude that laboratory Vis-NIR spectroscopy adds critical information that significantly improves the prediction accuracy of SOC compared to using RS data alone. We recommend the incorporation of laboratory spectra with RS data and other environmental data to improve soil spatial modeling and digital soil mapping (DSM).

Large topsoil organic carbon variability is controlled by Andisol properties and effectively assessed by VNIR spectroscopy in a coffee agroforestry system of Costa Rica

Assessing the spatial variability of soil organic carbon (SOC) is crucial for SOC monitoring and comparing management options. Topsoil (0–5cm) SOC concentrations were surveyed in a coffee agroforestry watershed (0.9km2) on Andisols in Costa Rica with uniform farm management. We encountered high values and large spatial variations of SOC, from 48.1 to 172gkg−1 in the dry combustion set (SOCref; n=72) used for calibrating the visible-near-infrared reflectance spectroscopy (VNIRS) samples (SOCVNIRS; 350–2500nm; n=520). VNIRS using partial least squares regression was effective in predicting SOC (R2=0.85; a root mean square error (RMSE)=12.3gkg−1) and proved an effective proxy measurement. We assessed several topographic, vegetation and andic soil property variables, of which only the latter (metal–humus complexes and allophanes) displayed strong correlations with SOCref concentrations. We compared Random Forest and three geostatistical approaches for the interpolation of SOC in unsampled locations. Ordinary kriging with SOCref yielded an RMSE of 28.0gkg−1. Random Forest was successful in incorporating many weakly and non-linearly correlated covariates with SOC (RMSE=14.7gkg−1), provided Alp (the sodium pyrophosphate extractable aluminum), the best predictor of SOC (r=0.85) but also the most costly variable to acquire. Co-kriging with Alp also showed high reduction in RMSE (16.0gkg−1). Co-kriging with SOCVNIRS only showed marginal reduction in RMSE to 24.2gkg−1 due to the presence of a high nugget effect.Local variability of SOC in this volcanic agroforestry watershed was dominated by andic properties whereas topographic or vegetation variables had very little impact. Estimation of SOC variability is recommended using inexpensive proxy measurements like VNIRS (RMSE=12.3gkg−1) rather than spatial interpolation techniques.

Rapid identification of soil cadmium pollution risk at regional scale based on visible and near-infrared spectroscopy

Soil heavy metal pollution due to long-term sewage irrigation is a serious environmental problem in many irrigation areas in northern China. Quickly identifying its pollution status is an important basis for remediation. Visible-near-infrared reflectance spectroscopy (VNIRS) provides a useful tool. In a case study, 76 soil samples were collected and their reflectance spectra were used to estimate cadmium (Cd) concentration by partial least squares regression (PLSR) and back propagation neural network (BPNN). To reduce noise, six pre-treatments were compared, in which orthogonal signal correction (OSC) was first used in soil Cd estimation. Spectral analysis and geostatistics were combined to identify Cd pollution hotspots. Results showed that Cd was accumulated in topsoil at the study area. OSC can effectively remove irrelevant information to improve prediction accuracy. More accurate estimation was achieved by applying a BPNN. Soil Cd pollution hotspots could be identified by interpolating the predicted values obtained from spectral estimates.

Development of hyperspectral model for rapid monitoring of soil organic carbon under precision farming in the Indo-Gangetic Plains of Punjab, India

Degrading soil quality at an alarming rate as a result of high input agriculture under continuous rice-wheat cropping system in the Indo-Gangetic alluvial Plains of Punjab (India), a major food growing region of south-east Asia, has ushered the need of precision farming for which rapid site specific monitoring of soil organic carbon (an indicator of soil quality) is needed. In this study, visible-near infrared reflectance spectroscopy was evaluated for rapid prediction of soil organic carbon (SOC) contents in soils of the Indo-Gangetic alluvial Plains of Punjab, India. A total of 800 surface soil samples (480 for calibration and 320 for validation) from farmers’ field representing the districts of Ludhiana, Moga, Gurdaspur and Bhatinda in Punjab State, India were collected, ensuring sufficient variation in SOC content. Reflectance spectra were obtained from air-dried samples (<2 mm size) under controlled laboratory conditions using a hyperspectral ASD FieldSpecPro spectroradiometer. Part of the same samples was used for SOC determination by Walkley and Black titration method. The SOC value in the study area varies from 4.0 to 18.1 g kg−1 (mean 7.9 g kg−1 and standard deviation of 2.2 g kg−1) among the soil samples. Partial least squares regression technique was employed to examine the relationships between SOC and the reflectance spectra; and to identify the wavelengths sensitive to SOC variation. Among 15 spectral transformations used for calibration, SGF-2-3 transformation (transformation to 1st derivative with second order polynomial smoothing with 3 points using Savitzky-Golay filter) was the best for SOC modeling in the IGP soils as it showed highest validation r2 (0.81) and RPD (2.30) and the lowest RMSEP (0.116) with 6 PLS factors. The most important wavelengths for SOC prediction were 460, 470 and 550 nm in the visible and 1400, 1420, 1920, 2040, 2210, 2270, 2320 and 2380 nm in the near-infrared region. At this juncture of much awaited second green revolution envisaged to be based on sustainability and precision agriculture in one hand and the increased availability of high resolution hyperspectral satellite data on the other hand; our findings regarding rapid evaluation of SOC through hyperspectral model are encouraging as it might assist in real time evaluation of pre and post-scenarios of soil quality and sustainability under precision farming system.

Visible–NIR reflectance spectroscopy and manifold learning methods applied to the detection of fungal infections on citrus fruit

The development of systems for automatically detecting decay in citrus fruit during quality control is still a challenge for the citrus industry. The feasibility of reflectance spectroscopy in the visible and near infrared (NIR) regions was evaluated for the automatic detection of the early symptoms of decay caused by Penicillium digitatum fungus in citrus fruit. Reflectance spectra of sound and decaying surface parts of mandarins cv. ‘Clemenvilla’ were acquired in two different spectral regions, from 650nm to 1050nm (visible–NIR) and from 1000nm to 1700nm (NIR), pointing to significant differences in spectra between sound and decaying skin for both spectral ranges. Three different manifold learning methods (principal component analysis, factor analysis and Sammon mapping) were investigated to transform the high-dimensional spectral data into meaningful representations of reduced dimensionality containing the essential information. The low-dimensional data representations were used as input feature vectors to discriminate between sound and decaying skin using a supervised classifier based on linear discriminant analysis. The best classification results were achieved by employing factor analysis on the NIR spectra, yielding a maximum overall classification accuracy of 97.8%, with a percentage of well-classified sound and decaying samples of 100% and 94.4%, respectively. These results lay the foundation for the future implementation of reflectance spectroscopy technology on a commercial fruit sorter for the purpose of detecting decay in citrus fruit.

Estimation of potentially toxic elements contamination in anthropogenic soils on a brown coal mining dumpsite by reflectance spectroscopy: a case study.

In order to monitor Potentially Toxic Elements (PTEs) in anthropogenic soils on brown coal mining dumpsites, a large number of samples and cumbersome, time-consuming laboratory measurements are required. Due to its rapidity, convenience and accuracy, reflectance spectroscopy within the Visible-Near Infrared (Vis-NIR) region has been used to predict soil constituents. This study evaluated the suitability of Vis-NIR (350–2500 nm) reflectance spectroscopy for predicting PTEs concentration, using samples collected on large brown coal mining dumpsites in the Czech Republic. Partial Least Square Regression (PLSR) and Support Vector Machine Regression (SVMR) with cross-validation were used to relate PTEs data to the reflectance spectral data by applying different preprocessing strategies. According to the criteria of minimal Root Mean Square Error of Prediction of Cross Validation (RMSEPcv) and maximal coefficient of determination (R2 cv) and Residual Prediction Deviation (RPD), the SVMR models with the first derivative pretreatment provided the most accurate prediction for As (R2 cv) = 0.89, RMSEPcv = 1.89, RPD = 2.63). Less accurate, but acceptable prediction for screening purposes for Cd and Cu (0.66 ˂ R2 cv) ˂ 0.81, RMSEPcv = 0.0.8 and 4.08 respectively, 2.0 ˂ RPD ˂ 2.5) were obtained. The PLSR model for predicting Mn (R2 cv) = 0.44, RMSEPcv = 116.43, RPD = 1.45) presented an inadequate model. Overall, SVMR models for the Vis-NIR spectra could be used indirectly for an accurate assessment of PTEs’ concentrations.

Characteristics of colored passive layers on titanium: morphology, optical properties, and corrosion resistance.

Electrochemically formed colored passive layers on titanium and their optical, surface morphology, and corrosion properties are presented and discussed. With the application of progressively higher AC voltages (VAC) during preparation of these passive layers, they are found to become more protective of the underlying metal, as determined from corrosion resistance measurements employing electrochemical polarization curve and inductively coupled plasma mass spectrometry experiments. The passive layers on titanium were found to be uniform in their surface morphology with no apparent cracks or pits. Surface morphology, and its relation to optical properties, was also investigated using visible light microscopy, profilometry, and near-infrared ultraviolet visible reflectance spectroscopy measurements. A correlation between the light reflected from the entire sample surface and the coloration of surface grains was also observed through these measurements. The reflectance spectra showed a red-shift of wavelength maxima (λmax) values as AC voltages and, therefore, thicknesses were increased. Overall, these passive layers are protective of an already remarkable metal, and with greater knowledge of the properties of colored protective layers, their potential may be employed in a wide range of applications.